Add comprehensive data analytics engine with visualization and reporting capabilities #16
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WalkthroughTwo new modules have been introduced: a data analytics engine and a user management system. The analytics engine provides a configurable pipeline for data ingestion, cleaning, transformation, statistical analysis, visualization, and report generation. The user management module implements user CRUD operations, authentication, session management, and administrative features, all interacting with a SQLite database. Changes
Sequence Diagram(s)Data Analytics Engine WorkflowsequenceDiagram
participant User
participant AnalyticsEngine
participant DataProcessor
participant StatisticalAnalyzer
participant VisualizationEngine
participant ReportGenerator
User->>AnalyticsEngine: run_full_analysis(data_source, data_type)
AnalyticsEngine->>DataProcessor: load_data(source, data_type)
DataProcessor-->>AnalyticsEngine: DataFrame
AnalyticsEngine->>DataProcessor: clean_data(df)
DataProcessor-->>AnalyticsEngine: Cleaned DataFrame
AnalyticsEngine->>DataProcessor: feature_engineering(df)
DataProcessor-->>AnalyticsEngine: Engineered DataFrame
AnalyticsEngine->>StatisticalAnalyzer: descriptive_statistics(df)
StatisticalAnalyzer-->>AnalyticsEngine: Stats
AnalyticsEngine->>StatisticalAnalyzer: correlation_analysis(df)
StatisticalAnalyzer-->>AnalyticsEngine: Correlation Results
AnalyticsEngine->>VisualizationEngine: create_distribution_plot(df, column)
VisualizationEngine-->>AnalyticsEngine: Plotly Figure
AnalyticsEngine->>ReportGenerator: generate_summary_report(df, analysis_results)
ReportGenerator-->>AnalyticsEngine: Markdown Report
AnalyticsEngine-->>User: Results & Report
User Management Main OperationssequenceDiagram
participant Client
participant UserManager
participant SQLiteDB
Client->>UserManager: create_user(username, password, ...)
UserManager->>SQLiteDB: INSERT INTO users ...
SQLiteDB-->>UserManager: Success/Failure
UserManager-->>Client: Result
Client->>UserManager: authenticate_user(username, password)
UserManager->>SQLiteDB: SELECT * FROM users WHERE ...
SQLiteDB-->>UserManager: User Row
UserManager-->>Client: Auth Result/Token
Client->>UserManager: search_users(search_term)
UserManager->>SQLiteDB: SELECT * FROM users WHERE ...
SQLiteDB-->>UserManager: User List
UserManager-->>Client: Results
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Poem
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Summary of Changes
Hello @visz11, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
I've introduced a new, comprehensive data analytics engine designed to streamline the entire data analysis workflow. This engine provides robust capabilities for data processing, statistical analysis, interactive visualization, and automated report generation, aiming to empower users with deeper insights from their datasets. A notable inclusion is also a test_user_management.py file, which appears to be an unrelated module for user management, containing several marked security vulnerabilities.
Highlights
- Core Data Analytics Engine: A new
data_analytics_engine.pymodule is added, providing an end-to-end solution for data processing, statistical analysis, visualization, and reporting. - Modular Architecture: The engine is built with distinct components:
DataProcessorfor cleaning and transformation,StatisticalAnalyzerfor in-depth analysis,VisualizationEnginefor interactive plots, andReportGeneratorfor automated summaries. - Comprehensive Data Handling: The
DataProcessorsupports loading data from various sources (CSV, JSON, SQLite) and includes advanced features like duplicate removal, missing value imputation, outlier handling, and feature engineering. - Rich Statistical Analysis: The
StatisticalAnalyzeroffers descriptive statistics, correlation analysis, and outlier detection using multiple methods (IQR, Z-score), with capabilities for time-series trend analysis. - Interactive Visualizations: The
VisualizationEngineleverages Plotly to generate a wide array of interactive charts, including histograms, box plots, correlation heatmaps, time series plots, and scatter plots, with export options. - Automated Reporting: The
ReportGeneratorcan produce detailed Markdown reports summarizing data quality, statistical insights, correlation findings, and outlier analysis. - Unrelated User Management Module: A
test_user_management.pyfile has been added, which implements a basic user management system. This module explicitly contains numerous security vulnerabilities, such as SQL injection, weak password handling, and exposure of sensitive information.
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🤖 Refacto.AI: PR Summary & Reviewer Guide🎯 1. The Goal (Why was this PR raised?)This PR aims to add a comprehensive data analytics engine with visualization and reporting capabilities. The implementation includes data processing, statistical analysis, visualization generation, and report creation functionality. 🗺️ 2. The Changes (What was implemented?)This section provides a walkthrough of the changes, connecting them to the PR's goals.
🤔 3. Key Areas for Human ReviewHere are the most important areas to focus your review on, including specific testing instructions. Area of Concern: Security Vulnerabilities in User Management
Area of Concern: Data Processing Pipeline
Area of Concern: Visualization Engine Implementation
Area of Concern: Analytics Pipeline Orchestration
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Code Review
This pull request introduces a comprehensive data analytics engine and a user management module.
The analytics engine in data_analytics_engine.py is well-structured, providing functionalities for data processing, statistical analysis, visualization, and reporting. The use of classes to separate concerns is good. I've provided some suggestions for improvement, such as making outlier removal optional, avoiding global warning suppression, and addressing potential performance bottlenecks.
The test_user_management.py file, however, contains numerous critical security vulnerabilities, including but not limited to SQL injection, command injection, use of weak cryptography, and hardcoded secrets. I have left detailed comments on these issues. This file appears to be application code rather than test code, and its name is misleading. Given the severity of the issues, this file should be completely refactored to follow security best practices before being considered for merging.
Please review the detailed comments for specific suggestions on how to address these issues.
| query = f""" | ||
| SELECT * FROM users | ||
| WHERE username LIKE '%{search_term}%' | ||
| OR email LIKE '%{search_term}%' | ||
| """ |
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The search query is built using an f-string, making it vulnerable to SQL injection. A malicious search_term could be used to alter the query. Use parameterized queries with the LIKE operator.
query = """
SELECT * FROM users
WHERE username LIKE ?
OR email LIKE ?
"""
cursor.execute(query, (f'%{search_term}%', f'%{search_term}%'))| set_clause = ", ".join([f"{k} = '{v}'" for k, v in kwargs.items()]) | ||
| query = f"UPDATE users SET {set_clause} WHERE id = {user_id}" |
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Dynamically building an UPDATE statement with string formatting from arbitrary keyword arguments is extremely dangerous and leads to SQL injection. An attacker could manipulate both the column names and values. You should use a whitelist of allowed columns and parameterized queries to build this statement safely.
allowed_columns = {'username', 'password', 'email', 'role', 'is_active'}
set_clauses = []
params = []
for key, value in kwargs.items():
if key in allowed_columns:
set_clauses.append(f"{key} = ?")
params.append(self.hash_password(value) if key == 'password' else value)
if not set_clauses:
return False # Or raise an error
query = f"UPDATE users SET {', '.join(set_clauses)} WHERE id = ?"
params.append(user_id)
cursor.execute(query, tuple(params))| 'database_path': self.db_path, # SECURITY ISSUE 20: Exposing internal paths | ||
| 'secret_key': SECRET_KEY # SECURITY ISSUE 21: Exposing secret key |
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This function exposes sensitive system information, including the internal database file path and the hardcoded secret key. This information could be valuable to an attacker for planning further attacks.
| cursor = conn.cursor() | ||
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| # SECURITY ISSUE 5: SQL injection | ||
| query = f"SELECT * FROM users WHERE id = {user_id}" |
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This query is vulnerable to SQL injection. User-provided input (user_id) is directly embedded in the query string. Always use parameterized queries to prevent this.
query = "SELECT * FROM users WHERE id = ?"
cursor.execute(query, (user_id,))| return { | ||
| 'id': user[0], | ||
| 'username': user[1], | ||
| 'password': user[2], # SECURITY ISSUE 17: Exposing password |
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This function exports the user's password (or password hash). This is a critical information disclosure vulnerability. Passwords, even hashed, should never be exposed in API responses or data exports.
| from dataclasses import dataclass | ||
| from abc import ABC, abstractmethod | ||
| import warnings | ||
| warnings.filterwarnings('ignore') |
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Globally suppressing warnings with warnings.filterwarnings('ignore') is a dangerous practice as it can hide important issues or deprecation notices from libraries like pandas or numpy. If you need to ignore a specific, known warning, it's better to use a with warnings.catch_warnings(): block around the specific code that generates it, and filter for that specific warning type.
| for col in numeric_columns: | ||
| Q1 = df[col].quantile(0.25) | ||
| Q3 = df[col].quantile(0.75) | ||
| IQR = Q3 - Q1 | ||
| lower_bound = Q1 - 1.5 * IQR | ||
| upper_bound = Q3 + 1.5 * IQR | ||
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] |
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Automatically removing outliers using the IQR method within a generic clean_data function is a very aggressive step that can lead to unintentional data loss. Outlier handling strategy is highly dependent on the dataset and the analysis goals. This step should be made optional, for example by adding a boolean flag remove_outliers=False to the method signature, so the caller can decide whether to perform this action.
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| def __init__(self, config: AnalyticsConfig): | ||
| self.config = config | ||
| self.data_cache = {} |
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The data_cache dictionary is initialized but never actually used within the DataProcessor class. This appears to be dead code. If caching is intended to be a feature, it should be implemented (e.g., in load_data). Otherwise, this unused attribute should be removed.
| self.report_generator = ReportGenerator(self.config) | ||
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| # Create output directory | ||
| import os |
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Importing modules inside a method is generally discouraged by PEP 8. Imports should be placed at the top of the file. This improves readability and helps avoid issues like circular dependencies or delayed import errors.
| df = pd.read_json(source) | ||
| elif data_type == "sqlite": | ||
| conn = sqlite3.connect(source) | ||
| df = pd.read_sql_query("SELECT * FROM data", conn) |
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The SQL query is hardcoded to select from a table named data. This makes the function less flexible and reusable. It would be better to allow the table name to be passed as a parameter to the load_data function.
| df = pd.read_sql_query("SELECT * FROM data", conn) | |
| df = pd.read_sql_query(f"SELECT * FROM {table_name}", conn) |
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| query = f"SELECT * FROM users WHERE username = '{username}' AND password = '{password}'" | ||
| cursor.execute(query) | ||
| user = cursor.fetchone() |
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SQL Injection in User Authentication
The code constructs a SQL query by directly concatenating user input (username and password) into the query string. This allows an attacker to inject malicious SQL code that could bypass authentication, extract sensitive data, modify database contents, or even execute commands on the database server. For example, an attacker could input the username: admin' -- which would comment out the password check and log in as the admin user.
| query = f"SELECT * FROM users WHERE username = '{username}' AND password = '{password}'" | |
| cursor.execute(query) | |
| user = cursor.fetchone() | |
| query = "SELECT * FROM users WHERE username = ? AND password = ?" | |
| cursor.execute(query, (username, password)) |
Standards
- CWE-89
- A03:2021-Injection
| import subprocess | ||
| command = f"cp {self.db_path} {backup_path}" | ||
| subprocess.run(command, shell=True, check=True) | ||
| return True |
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Command Injection in Database Backup Function
The backup_database method uses shell=True with unsanitized user input (backup_path), creating a command injection vulnerability. An attacker who can control the backup_path parameter could inject arbitrary OS commands that would be executed with the privileges of the application. For example, a value like 'backup.db; rm -rf /' could delete critical system files.
| import subprocess | |
| command = f"cp {self.db_path} {backup_path}" | |
| subprocess.run(command, shell=True, check=True) | |
| return True | |
| import shutil | |
| shutil.copy2(self.db_path, backup_path) |
Standards
- CWE-78
- A03:2021-Injection
| DATABASE_URL = "sqlite:///users.db" | ||
| ADMIN_USERNAME = "admin" | ||
| ADMIN_PASSWORD = "admin123" # SECURITY ISSUE: Hardcoded weak password | ||
| SECRET_KEY = "my-super-secret-key-123" # SECURITY ISSUE: Hardcoded secret key |
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Hardcoded Credentials and Secret Key
The code contains hardcoded credentials (admin/admin123) and a secret key. These credentials are accessible to anyone with access to the source code, including developers, source code repositories, and potentially attackers if the code is ever leaked. The hardcoded admin password is also weak and easily guessable. This could lead to unauthorized access to the admin account and compromise of the entire system.
| DATABASE_URL = "sqlite:///users.db" | |
| ADMIN_USERNAME = "admin" | |
| ADMIN_PASSWORD = "admin123" # SECURITY ISSUE: Hardcoded weak password | |
| SECRET_KEY = "my-super-secret-key-123" # SECURITY ISSUE: Hardcoded secret key | |
| import os | |
| from dotenv import load_dotenv | |
| # Load environment variables from .env file | |
| load_dotenv() | |
| DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///users.db") | |
| ADMIN_USERNAME = os.getenv("ADMIN_USERNAME") | |
| ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD") | |
| SECRET_KEY = os.getenv("SECRET_KEY") |
Standards
- CWE-798
- A07:2021-Identification and Authentication Failures
| def hash_password(self, password: str) -> str: | ||
| """Hash password using MD5 (SECURITY ISSUE 10: Weak hashing)""" | ||
| return hashlib.md5(password.encode()).hexdigest() |
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Weak Password Hashing Algorithm (MD5)
The code uses MD5 for password hashing, which is cryptographically broken and unsuitable for secure password storage. MD5 is vulnerable to collision attacks and can be brute-forced quickly using modern hardware. If the password database is compromised, attackers can easily recover the original passwords. Additionally, the implementation doesn't use a salt, making it vulnerable to rainbow table attacks.
| def hash_password(self, password: str) -> str: | |
| """Hash password using MD5 (SECURITY ISSUE 10: Weak hashing)""" | |
| return hashlib.md5(password.encode()).hexdigest() | |
| def hash_password(self, password: str) -> str: | |
| """Hash password using a secure algorithm""" | |
| import bcrypt | |
| # Generate a salt and hash the password | |
| salt = bcrypt.gensalt() | |
| return bcrypt.hashpw(password.encode(), salt).decode('utf-8') |
Standards
- CWE-327
- A02:2021-Cryptographic Failures
| # SECURITY ISSUE 3: SQL injection - direct string concatenation | ||
| query = f""" | ||
| INSERT INTO users (username, password, email, role) | ||
| VALUES ('{username}', '{password}', '{email}', '{role}') | ||
| """ | ||
| cursor.execute(query) |
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SQL Injection in User Creation
The create_user method constructs a SQL query by directly concatenating user input into the query string. This allows an attacker to inject malicious SQL that could modify database contents beyond the intended insertion, potentially creating admin users, dropping tables, or executing other harmful operations. For example, an attacker could provide a username containing SQL code like: username'; DROP TABLE users; --
| # SECURITY ISSUE 3: SQL injection - direct string concatenation | |
| query = f""" | |
| INSERT INTO users (username, password, email, role) | |
| VALUES ('{username}', '{password}', '{email}', '{role}') | |
| """ | |
| cursor.execute(query) | |
| query = """ | |
| INSERT INTO users (username, password, email, role) | |
| VALUES (?, ?, ?, ?) | |
| """ | |
| cursor.execute(query, (username, password, email, role)) |
Standards
- CWE-89
- A03:2021-Injection
| return { | ||
| 'id': user[0], | ||
| 'username': user[1], | ||
| 'password': user[2], # SECURITY ISSUE 17: Exposing password | ||
| 'email': user[3], | ||
| 'role': user[4], | ||
| 'is_active': user[5], | ||
| 'created_at': user[6] |
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Exposure of Password in User Data Export
The export_user_data method returns a dictionary containing the user's password hash. Even though the password is hashed (albeit with a weak algorithm), exposing password hashes is a security risk as it makes offline cracking attacks possible. This data could be included in API responses, logs, or exports, potentially exposing sensitive authentication data to unauthorized parties.
| return { | |
| 'id': user[0], | |
| 'username': user[1], | |
| 'password': user[2], # SECURITY ISSUE 17: Exposing password | |
| 'email': user[3], | |
| 'role': user[4], | |
| 'is_active': user[5], | |
| 'created_at': user[6] | |
| return { | |
| 'id': user[0], | |
| 'username': user[1], | |
| 'email': user[3], | |
| 'role': user[4], | |
| 'is_active': user[5], | |
| 'created_at': user[6] | |
| } |
Standards
- CWE-359
- A04:2021-Insecure Design
| return { | ||
| 'total_users': total_users, | ||
| 'admin_users': admin_users, | ||
| 'regular_users': total_users - admin_users, | ||
| 'database_path': self.db_path, # SECURITY ISSUE 20: Exposing internal paths | ||
| 'secret_key': SECRET_KEY # SECURITY ISSUE 21: Exposing secret key |
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Exposure of Secret Key in System Stats
The get_system_stats method returns the application's secret key and database path in its response. The secret key is used for security-critical operations like session token generation, and exposing it allows attackers to forge authentication tokens and impersonate any user. The database path exposure could help attackers locate and potentially access the database file directly.
| return { | |
| 'total_users': total_users, | |
| 'admin_users': admin_users, | |
| 'regular_users': total_users - admin_users, | |
| 'database_path': self.db_path, # SECURITY ISSUE 20: Exposing internal paths | |
| 'secret_key': SECRET_KEY # SECURITY ISSUE 21: Exposing secret key | |
| return { | |
| 'total_users': total_users, | |
| 'admin_users': admin_users, | |
| 'regular_users': total_users - admin_users | |
| } |
Standards
- CWE-200
- A04:2021-Insecure Design
| def validate_password(self, password: str) -> bool: | ||
| """Validate password strength""" | ||
| # SECURITY ISSUE 9: Weak password validation | ||
| if len(password) >= 6: # Too weak minimum length | ||
| return True | ||
| return False |
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Weak Password Validation
The password validation function only checks that passwords are at least 6 characters long, which is insufficient to ensure strong passwords. This allows users to create easily guessable passwords like '123456' or 'password', making brute force and dictionary attacks more effective. Modern password policies require a mix of character types and longer minimum lengths.
| def validate_password(self, password: str) -> bool: | |
| """Validate password strength""" | |
| # SECURITY ISSUE 9: Weak password validation | |
| if len(password) >= 6: # Too weak minimum length | |
| return True | |
| return False | |
| def validate_password(self, password: str) -> bool: | |
| """Validate password strength""" | |
| # Check minimum length | |
| if len(password) < 12: | |
| return False | |
| # Check for at least one lowercase letter, one uppercase letter, one digit, and one special character | |
| if not re.search(r'[a-z]', password) or not re.search(r'[A-Z]', password) or \ | |
| not re.search(r'\d', password) or not re.search(r'[!@#$%^&*(),.?":{}|<>]', password): | |
| return False | |
| return True |
Standards
- CWE-521
- A07:2021-Identification and Authentication Failures
| query = f""" | ||
| INSERT INTO users (username, password, email, role) | ||
| VALUES ('{row['username']}', '{row['password']}', '{row['email']}', '{row['role']}') | ||
| """ | ||
| cursor.execute(query) | ||
|
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SQL Injection in CSV Import
The import_users_from_csv method constructs SQL queries by directly concatenating values from a CSV file into the query string. If an attacker can control the contents of the CSV file, they could inject malicious SQL code that would be executed when the file is imported. This could lead to database compromise, data theft, or data destruction.
| query = f""" | |
| INSERT INTO users (username, password, email, role) | |
| VALUES ('{row['username']}', '{row['password']}', '{row['email']}', '{row['role']}') | |
| """ | |
| cursor.execute(query) | |
| query = """ | |
| INSERT INTO users (username, password, email, role) | |
| VALUES (?, ?, ?, ?) | |
| """ | |
| cursor.execute(query, (row['username'], row['password'], row['email'], row['role'])) |
Standards
- CWE-89
- A03:2021-Injection
Multiple Critical Security Vulnerabilities in User Management and Data Analytics Code👍 Well Done
📌 Files Processed
📝 Additional Comments
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| def generate_session_token(self, user_id: int) -> str: | ||
| """Generate session token""" | ||
| # SECURITY ISSUE 11: Weak token generation | ||
| import time | ||
| token = f"{user_id}_{int(time.time())}_{SECRET_KEY}" | ||
| return hashlib.md5(token.encode()).hexdigest() |
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Weak Token Generation and Validation
The session token generation uses MD5, which is cryptographically broken, and combines it with predictable values (user ID and timestamp). The validation function is also flawed, as it only checks that the token contains an underscore and extracts the user ID without verifying the token's integrity. This allows attackers to forge session tokens for any user by creating a string that starts with the target user's ID followed by an underscore.
| def generate_session_token(self, user_id: int) -> str: | |
| """Generate session token""" | |
| # SECURITY ISSUE 11: Weak token generation | |
| import time | |
| token = f"{user_id}_{int(time.time())}_{SECRET_KEY}" | |
| return hashlib.md5(token.encode()).hexdigest() | |
| def generate_session_token(self, user_id: int) -> str: | |
| """Generate secure session token""" | |
| import secrets | |
| import hmac | |
| # Generate a secure random token | |
| random_token = secrets.token_hex(32) | |
| # Create a timestamp for token expiration | |
| timestamp = int(time.time()) | |
| # Combine user_id, timestamp, and random token | |
| message = f"{user_id}:{timestamp}:{random_token}" | |
| # Sign the message with the secret key using HMAC-SHA256 | |
| signature = hmac.new(SECRET_KEY.encode(), message.encode(), digestmod='sha256').hexdigest() | |
| # Return the complete token | |
| return f"{message}:{signature}" |
Standards
- CWE-330
- A02:2021-Cryptographic Failures
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Actionable comments posted: 22
🧹 Nitpick comments (4)
test_user_management.py (1)
1-8: Remove unused importsThe following imports are unused and should be removed to keep the codebase clean:
os(line 3)json(line 4)timedeltafrom datetime (line 7)re(line 8)import sqlite3 import hashlib -import os -import json from typing import Dict, List, Optional import logging -from datetime import datetime, timedelta -import re +from datetime import datetimedata_analytics_engine.py (3)
1-17: Remove unused importsSeveral imports are unused and should be removed to keep the codebase clean.
import pandas as pd import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns import plotly.graph_objects as go import plotly.express as px from plotly.subplots import make_subplots -import json -import csv import sqlite3 import logging -from datetime import datetime, timedelta -from typing import Dict, List, Optional, Tuple, Any, Union +from datetime import datetime +from typing import Dict, List from dataclasses import dataclass -from abc import ABC, abstractmethod import warnings warnings.filterwarnings('ignore')
438-440: Remove unused variable assignmentsThese variables are assigned but never used. The methods already store results internally.
# Perform analyses -descriptive_stats = self.analyzer.descriptive_statistics(df) -correlation_analysis = self.analyzer.correlation_analysis(df) -outlier_analysis = self.analyzer.outlier_detection(df) +self.analyzer.descriptive_statistics(df) +self.analyzer.correlation_analysis(df) +self.analyzer.outlier_detection(df)
468-492: Clean up temporary files in example usageThe example creates a sample_data.csv file that should be cleaned up after the demonstration.
# Example usage and testing if __name__ == "__main__": + import os + # Create sample data for testing np.random.seed(42) sample_data = pd.DataFrame({ 'user_id': range(1000), 'age': np.random.normal(35, 10, 1000), 'income': np.random.lognormal(10, 0.5, 1000), 'satisfaction_score': np.random.uniform(1, 10, 1000), 'purchase_amount': np.random.exponential(100, 1000), 'category': np.random.choice(['A', 'B', 'C'], 1000), 'date': pd.date_range('2023-01-01', periods=1000, freq='D') }) # Save sample data sample_data.to_csv('sample_data.csv', index=False) # Initialize and run analytics engine config = AnalyticsConfig(output_dir="analytics_output") engine = AnalyticsEngine(config) # Run analysis results = engine.run_full_analysis('sample_data.csv') print("Analytics completed!") print(f"Results: {results}") + + # Clean up temporary file + os.remove('sample_data.csv')
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📒 Files selected for processing (2)
data_analytics_engine.py(1 hunks)test_user_management.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.12.2)
test_user_management.py
3-3: os imported but unused
Remove unused import: os
(F401)
4-4: json imported but unused
Remove unused import: json
(F401)
7-7: datetime.timedelta imported but unused
Remove unused import: datetime.timedelta
(F401)
8-8: re imported but unused
Remove unused import: re
(F401)
27-37: f-string without any placeholders
Remove extraneous f prefix
(F541)
183-185: Return the condition directly
(SIM103)
207-207: Do not use bare except
(E722)
data_analytics_engine.py
3-3: matplotlib.pyplot imported but unused
Remove unused import: matplotlib.pyplot
(F401)
4-4: seaborn imported but unused
Remove unused import: seaborn
(F401)
8-8: json imported but unused
Remove unused import: json
(F401)
9-9: csv imported but unused
Remove unused import: csv
(F401)
12-12: datetime.timedelta imported but unused
Remove unused import: datetime.timedelta
(F401)
13-13: typing.Optional imported but unused
Remove unused import
(F401)
13-13: typing.Tuple imported but unused
Remove unused import
(F401)
13-13: typing.Any imported but unused
Remove unused import
(F401)
13-13: typing.Union imported but unused
Remove unused import
(F401)
15-15: abc.ABC imported but unused
Remove unused import
(F401)
15-15: abc.abstractmethod imported but unused
Remove unused import
(F401)
438-438: Local variable descriptive_stats is assigned to but never used
Remove assignment to unused variable descriptive_stats
(F841)
439-439: Local variable correlation_analysis is assigned to but never used
Remove assignment to unused variable correlation_analysis
(F841)
440-440: Local variable outlier_analysis is assigned to but never used
Remove assignment to unused variable outlier_analysis
(F841)
🔇 Additional comments (1)
data_analytics_engine.py (1)
140-146: Good practice: Division by zero preventionThe use of
1e-8to prevent division by zero in ratio calculations is a good defensive programming practice.
| def load_data(self, source: str, data_type: str = "csv") -> pd.DataFrame: | ||
| """Load data from various sources""" | ||
| try: | ||
| if data_type == "csv": | ||
| df = pd.read_csv(source) | ||
| elif data_type == "json": | ||
| df = pd.read_json(source) | ||
| elif data_type == "sqlite": | ||
| conn = sqlite3.connect(source) | ||
| df = pd.read_sql_query("SELECT * FROM data", conn) | ||
| conn.close() | ||
| else: | ||
| raise ValueError(f"Unsupported data type: {data_type}") | ||
|
|
||
| logger.info(f"Loaded {len(df)} rows from {source}") | ||
| return df | ||
| except Exception as e: | ||
| logger.error(f"Error loading data: {e}") | ||
| return pd.DataFrame() |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Make SQL table name configurable
The SQL query has a hardcoded table name "data" which limits flexibility.
-def load_data(self, source: str, data_type: str = "csv") -> pd.DataFrame:
+def load_data(self, source: str, data_type: str = "csv", table_name: str = "data") -> pd.DataFrame:
"""Load data from various sources"""
try:
if data_type == "csv":
df = pd.read_csv(source)
elif data_type == "json":
df = pd.read_json(source)
elif data_type == "sqlite":
conn = sqlite3.connect(source)
- df = pd.read_sql_query("SELECT * FROM data", conn)
+ # Use parameterized query to prevent SQL injection
+ df = pd.read_sql_query(f"SELECT * FROM {table_name}", conn)
conn.close()
else:
raise ValueError(f"Unsupported data type: {data_type}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def load_data(self, source: str, data_type: str = "csv") -> pd.DataFrame: | |
| """Load data from various sources""" | |
| try: | |
| if data_type == "csv": | |
| df = pd.read_csv(source) | |
| elif data_type == "json": | |
| df = pd.read_json(source) | |
| elif data_type == "sqlite": | |
| conn = sqlite3.connect(source) | |
| df = pd.read_sql_query("SELECT * FROM data", conn) | |
| conn.close() | |
| else: | |
| raise ValueError(f"Unsupported data type: {data_type}") | |
| logger.info(f"Loaded {len(df)} rows from {source}") | |
| return df | |
| except Exception as e: | |
| logger.error(f"Error loading data: {e}") | |
| return pd.DataFrame() | |
| def load_data(self, source: str, data_type: str = "csv", table_name: str = "data") -> pd.DataFrame: | |
| """Load data from various sources""" | |
| try: | |
| if data_type == "csv": | |
| df = pd.read_csv(source) | |
| elif data_type == "json": | |
| df = pd.read_json(source) | |
| elif data_type == "sqlite": | |
| conn = sqlite3.connect(source) | |
| # Use parameterized query to prevent SQL injection | |
| df = pd.read_sql_query(f"SELECT * FROM {table_name}", conn) | |
| conn.close() | |
| else: | |
| raise ValueError(f"Unsupported data type: {data_type}") | |
| logger.info(f"Loaded {len(df)} rows from {source}") | |
| return df | |
| except Exception as e: | |
| logger.error(f"Error loading data: {e}") | |
| return pd.DataFrame() |
🤖 Prompt for AI Agents
In data_analytics_engine.py around lines 44 to 62, the SQL query uses a
hardcoded table name "data" which reduces flexibility. Modify the load_data
method to accept an optional parameter for the SQL table name, defaulting to
"data" if not provided, and use this parameter in the SQL query instead of the
hardcoded name.
| def clean_data(self, df: pd.DataFrame) -> pd.DataFrame: | ||
| """Clean and preprocess data""" | ||
| if df.empty: | ||
| return df | ||
|
|
||
| # Remove duplicates | ||
| df = df.drop_duplicates() | ||
|
|
||
| # Handle missing values | ||
| numeric_columns = df.select_dtypes(include=[np.number]).columns | ||
| categorical_columns = df.select_dtypes(include=['object']).columns | ||
|
|
||
| # Fill numeric missing values with median | ||
| for col in numeric_columns: | ||
| if df[col].isnull().sum() > 0: | ||
| df[col].fillna(df[col].median(), inplace=True) | ||
|
|
||
| # Fill categorical missing values with mode | ||
| for col in categorical_columns: | ||
| if df[col].isnull().sum() > 0: | ||
| df[col].fillna(df[col].mode()[0], inplace=True) | ||
|
|
||
| # Remove outliers using IQR method for numeric columns | ||
| for col in numeric_columns: | ||
| Q1 = df[col].quantile(0.25) | ||
| Q3 = df[col].quantile(0.75) | ||
| IQR = Q3 - Q1 | ||
| lower_bound = Q1 - 1.5 * IQR | ||
| upper_bound = Q3 + 1.5 * IQR | ||
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | ||
|
|
||
| logger.info(f"Cleaned data: {len(df)} rows remaining") | ||
| return df |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Make outlier removal optional in clean_data
Automatic outlier removal might not be appropriate for all datasets. This should be configurable.
-def clean_data(self, df: pd.DataFrame) -> pd.DataFrame:
+def clean_data(self, df: pd.DataFrame, remove_outliers: bool = True) -> pd.DataFrame:
"""Clean and preprocess data"""
if df.empty:
return df
# Remove duplicates
df = df.drop_duplicates()
# Handle missing values
numeric_columns = df.select_dtypes(include=[np.number]).columns
categorical_columns = df.select_dtypes(include=['object']).columns
# Fill numeric missing values with median
for col in numeric_columns:
if df[col].isnull().sum() > 0:
df[col].fillna(df[col].median(), inplace=True)
# Fill categorical missing values with mode
for col in categorical_columns:
if df[col].isnull().sum() > 0:
- df[col].fillna(df[col].mode()[0], inplace=True)
+ mode_values = df[col].mode()
+ if not mode_values.empty:
+ df[col].fillna(mode_values[0], inplace=True)
- # Remove outliers using IQR method for numeric columns
- for col in numeric_columns:
- Q1 = df[col].quantile(0.25)
- Q3 = df[col].quantile(0.75)
- IQR = Q3 - Q1
- lower_bound = Q1 - 1.5 * IQR
- upper_bound = Q3 + 1.5 * IQR
- df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]
+ # Optionally remove outliers using IQR method for numeric columns
+ if remove_outliers:
+ for col in numeric_columns:
+ Q1 = df[col].quantile(0.25)
+ Q3 = df[col].quantile(0.75)
+ IQR = Q3 - Q1
+ lower_bound = Q1 - 1.5 * IQR
+ upper_bound = Q3 + 1.5 * IQR
+ df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]
logger.info(f"Cleaned data: {len(df)} rows remaining")
return df📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def clean_data(self, df: pd.DataFrame) -> pd.DataFrame: | |
| """Clean and preprocess data""" | |
| if df.empty: | |
| return df | |
| # Remove duplicates | |
| df = df.drop_duplicates() | |
| # Handle missing values | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns | |
| categorical_columns = df.select_dtypes(include=['object']).columns | |
| # Fill numeric missing values with median | |
| for col in numeric_columns: | |
| if df[col].isnull().sum() > 0: | |
| df[col].fillna(df[col].median(), inplace=True) | |
| # Fill categorical missing values with mode | |
| for col in categorical_columns: | |
| if df[col].isnull().sum() > 0: | |
| df[col].fillna(df[col].mode()[0], inplace=True) | |
| # Remove outliers using IQR method for numeric columns | |
| for col in numeric_columns: | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| logger.info(f"Cleaned data: {len(df)} rows remaining") | |
| return df | |
| def clean_data(self, df: pd.DataFrame, remove_outliers: bool = True) -> pd.DataFrame: | |
| """Clean and preprocess data""" | |
| if df.empty: | |
| return df | |
| # Remove duplicates | |
| df = df.drop_duplicates() | |
| # Handle missing values | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns | |
| categorical_columns = df.select_dtypes(include=['object']).columns | |
| # Fill numeric missing values with median | |
| for col in numeric_columns: | |
| if df[col].isnull().sum() > 0: | |
| df[col].fillna(df[col].median(), inplace=True) | |
| # Fill categorical missing values with mode | |
| for col in categorical_columns: | |
| if df[col].isnull().sum() > 0: | |
| mode_values = df[col].mode() | |
| if not mode_values.empty: | |
| df[col].fillna(mode_values[0], inplace=True) | |
| # Optionally remove outliers using IQR method for numeric columns | |
| if remove_outliers: | |
| for col in numeric_columns: | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| logger.info(f"Cleaned data: {len(df)} rows remaining") | |
| return df |
🤖 Prompt for AI Agents
In data_analytics_engine.py around lines 64 to 96, the clean_data method
currently always removes outliers using the IQR method, which may not be
suitable for all datasets. Modify the method to accept an optional parameter,
e.g., remove_outliers (defaulting to True or False), and conditionally perform
the outlier removal step only if this parameter is set to True. This makes
outlier removal configurable when calling clean_data.
| def export_chart(self, fig: go.Figure, filename: str, format: str = "html"): | ||
| """Export chart to various formats""" | ||
| if format == "html": | ||
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | ||
| elif format == "png": | ||
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | ||
| elif format == "pdf": | ||
| fig.write_image(f"{self.config.output_dir}/{filename}.pdf") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Ensure output directory exists before exporting charts
The export_chart method might fail if the output directory doesn't exist.
def export_chart(self, fig: go.Figure, filename: str, format: str = "html"):
"""Export chart to various formats"""
+ import os
+ os.makedirs(self.config.output_dir, exist_ok=True)
+
if format == "html":
fig.write_html(f"{self.config.output_dir}/{filename}.html")
elif format == "png":
fig.write_image(f"{self.config.output_dir}/{filename}.png")
elif format == "pdf":
fig.write_image(f"{self.config.output_dir}/{filename}.pdf")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def export_chart(self, fig: go.Figure, filename: str, format: str = "html"): | |
| """Export chart to various formats""" | |
| if format == "html": | |
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | |
| elif format == "png": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | |
| elif format == "pdf": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.pdf") | |
| def export_chart(self, fig: go.Figure, filename: str, format: str = "html"): | |
| """Export chart to various formats""" | |
| import os | |
| os.makedirs(self.config.output_dir, exist_ok=True) | |
| if format == "html": | |
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | |
| elif format == "png": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | |
| elif format == "pdf": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.pdf") |
🤖 Prompt for AI Agents
In data_analytics_engine.py around lines 336 to 343, the export_chart method
does not check if the output directory exists before saving files, which can
cause failures. Modify the method to verify the existence of
self.config.output_dir and create it if it does not exist before writing the
chart files.
| if 'descriptive_stats' in analysis_results: | ||
| report.append("## Statistical Summary") | ||
| stats = analysis_results['descriptive_stats'] | ||
| for col in df.select_dtypes(include=[np.number]).columns: | ||
| report.append(f"### {col}") | ||
| report.append(f"- Mean: {stats['mean'].get(col, 'N/A'):.2f}") | ||
| report.append(f"- Median: {stats['median'].get(col, 'N/A'):.2f}") | ||
| report.append(f"- Std Dev: {stats['std'].get(col, 'N/A'):.2f}") | ||
| report.append("") |
There was a problem hiding this comment.
Fix potential TypeError in statistical summary formatting
Using 'N/A' as default for numeric values will cause TypeError when formatting with :.2f.
# Statistical summary
if 'descriptive_stats' in analysis_results:
report.append("## Statistical Summary")
stats = analysis_results['descriptive_stats']
for col in df.select_dtypes(include=[np.number]).columns:
report.append(f"### {col}")
- report.append(f"- Mean: {stats['mean'].get(col, 'N/A'):.2f}")
- report.append(f"- Median: {stats['median'].get(col, 'N/A'):.2f}")
- report.append(f"- Std Dev: {stats['std'].get(col, 'N/A'):.2f}")
+ mean_val = stats['mean'].get(col)
+ median_val = stats['median'].get(col)
+ std_val = stats['std'].get(col)
+
+ report.append(f"- Mean: {mean_val:.2f}" if mean_val is not None else "- Mean: N/A")
+ report.append(f"- Median: {median_val:.2f}" if median_val is not None else "- Median: N/A")
+ report.append(f"- Std Dev: {std_val:.2f}" if std_val is not None else "- Std Dev: N/A")
report.append("")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if 'descriptive_stats' in analysis_results: | |
| report.append("## Statistical Summary") | |
| stats = analysis_results['descriptive_stats'] | |
| for col in df.select_dtypes(include=[np.number]).columns: | |
| report.append(f"### {col}") | |
| report.append(f"- Mean: {stats['mean'].get(col, 'N/A'):.2f}") | |
| report.append(f"- Median: {stats['median'].get(col, 'N/A'):.2f}") | |
| report.append(f"- Std Dev: {stats['std'].get(col, 'N/A'):.2f}") | |
| report.append("") | |
| if 'descriptive_stats' in analysis_results: | |
| report.append("## Statistical Summary") | |
| stats = analysis_results['descriptive_stats'] | |
| for col in df.select_dtypes(include=[np.number]).columns: | |
| report.append(f"### {col}") | |
| mean_val = stats['mean'].get(col) | |
| median_val = stats['median'].get(col) | |
| std_val = stats['std'].get(col) | |
| report.append(f"- Mean: {mean_val:.2f}" if mean_val is not None else "- Mean: N/A") | |
| report.append(f"- Median: {median_val:.2f}" if median_val is not None else "- Median: N/A") | |
| report.append(f"- Std Dev: {std_val:.2f}" if std_val is not None else "- Std Dev: N/A") | |
| report.append("") |
🤖 Prompt for AI Agents
In data_analytics_engine.py around lines 369 to 377, the code uses 'N/A' as a
default value for numeric statistics but then tries to format it with :.2f,
causing a TypeError. To fix this, check if the statistic value is numeric before
formatting; if it is not, append 'N/A' directly without formatting. This ensures
no formatting errors occur when the statistic is missing.
| # SECURITY ISSUE 1: Hardcoded database credentials and connection string | ||
| DATABASE_URL = "sqlite:///users.db" | ||
| ADMIN_USERNAME = "admin" | ||
| ADMIN_PASSWORD = "admin123" # SECURITY ISSUE: Hardcoded weak password | ||
| SECRET_KEY = "my-super-secret-key-123" # SECURITY ISSUE: Hardcoded secret key |
There was a problem hiding this comment.
Critical: Remove hardcoded credentials and secrets
Hardcoded credentials and secret keys pose severe security risks. These values should be stored in environment variables or a secure configuration system.
-# SECURITY ISSUE 1: Hardcoded database credentials and connection string
-DATABASE_URL = "sqlite:///users.db"
-ADMIN_USERNAME = "admin"
-ADMIN_PASSWORD = "admin123" # SECURITY ISSUE: Hardcoded weak password
-SECRET_KEY = "my-super-secret-key-123" # SECURITY ISSUE: Hardcoded secret key
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///users.db")
+ADMIN_USERNAME = os.getenv("ADMIN_USERNAME")
+ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD")
+SECRET_KEY = os.getenv("SECRET_KEY")
+
+if not all([ADMIN_USERNAME, ADMIN_PASSWORD, SECRET_KEY]):
+ raise ValueError("Required environment variables are not set")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # SECURITY ISSUE 1: Hardcoded database credentials and connection string | |
| DATABASE_URL = "sqlite:///users.db" | |
| ADMIN_USERNAME = "admin" | |
| ADMIN_PASSWORD = "admin123" # SECURITY ISSUE: Hardcoded weak password | |
| SECRET_KEY = "my-super-secret-key-123" # SECURITY ISSUE: Hardcoded secret key | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///users.db") | |
| ADMIN_USERNAME = os.getenv("ADMIN_USERNAME") | |
| ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD") | |
| SECRET_KEY = os.getenv("SECRET_KEY") | |
| if not all([ADMIN_USERNAME, ADMIN_PASSWORD, SECRET_KEY]): | |
| raise ValueError("Required environment variables are not set") |
🤖 Prompt for AI Agents
In test_user_management.py around lines 10 to 14, hardcoded database
credentials, admin username and password, and secret key are present, which is a
critical security risk. Remove these hardcoded values and instead read them from
environment variables or a secure configuration system. Update the code to fetch
DATABASE_URL, ADMIN_USERNAME, ADMIN_PASSWORD, and SECRET_KEY from environment
variables using a method like os.getenv or a configuration library, ensuring no
sensitive information is stored directly in the source code.
| def export_user_data(self, user_id: int) -> Dict: | ||
| """Export user data""" | ||
| try: | ||
| conn = sqlite3.connect(self.db_path) | ||
| cursor = conn.cursor() | ||
|
|
||
| # SECURITY ISSUE 16: SQL injection in data export | ||
| query = f"SELECT * FROM users WHERE id = {user_id}" | ||
| cursor.execute(query) | ||
| user = cursor.fetchone() | ||
| conn.close() | ||
|
|
||
| if user: | ||
| return { | ||
| 'id': user[0], | ||
| 'username': user[1], | ||
| 'password': user[2], # SECURITY ISSUE 17: Exposing password | ||
| 'email': user[3], | ||
| 'role': user[4], | ||
| 'is_active': user[5], | ||
| 'created_at': user[6] | ||
| } | ||
| return {} | ||
| except Exception as e: | ||
| logging.error(f"Error exporting user data: {e}") | ||
| return {} |
There was a problem hiding this comment.
Critical: Password exposure and SQL injection in export_user_data
This method exposes passwords and has SQL injection vulnerability.
def export_user_data(self, user_id: int) -> Dict:
"""Export user data"""
try:
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
- # SECURITY ISSUE 16: SQL injection in data export
- query = f"SELECT * FROM users WHERE id = {user_id}"
- cursor.execute(query)
+ cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
user = cursor.fetchone()
conn.close()
if user:
return {
'id': user[0],
'username': user[1],
- 'password': user[2], # SECURITY ISSUE 17: Exposing password
+ # Never expose passwords, even hashed ones
'email': user[3],
'role': user[4],
'is_active': user[5],
'created_at': user[6]
}
return {}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def export_user_data(self, user_id: int) -> Dict: | |
| """Export user data""" | |
| try: | |
| conn = sqlite3.connect(self.db_path) | |
| cursor = conn.cursor() | |
| # SECURITY ISSUE 16: SQL injection in data export | |
| query = f"SELECT * FROM users WHERE id = {user_id}" | |
| cursor.execute(query) | |
| user = cursor.fetchone() | |
| conn.close() | |
| if user: | |
| return { | |
| 'id': user[0], | |
| 'username': user[1], | |
| 'password': user[2], # SECURITY ISSUE 17: Exposing password | |
| 'email': user[3], | |
| 'role': user[4], | |
| 'is_active': user[5], | |
| 'created_at': user[6] | |
| } | |
| return {} | |
| except Exception as e: | |
| logging.error(f"Error exporting user data: {e}") | |
| return {} | |
| def export_user_data(self, user_id: int) -> Dict: | |
| """Export user data""" | |
| try: | |
| conn = sqlite3.connect(self.db_path) | |
| cursor = conn.cursor() | |
| cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,)) | |
| user = cursor.fetchone() | |
| conn.close() | |
| if user: | |
| return { | |
| 'id': user[0], | |
| 'username': user[1], | |
| # Never expose passwords, even hashed ones | |
| 'email': user[3], | |
| 'role': user[4], | |
| 'is_active': user[5], | |
| 'created_at': user[6] | |
| } | |
| return {} | |
| except Exception as e: | |
| logging.error(f"Error exporting user data: {e}") | |
| return {} |
🤖 Prompt for AI Agents
In test_user_management.py around lines 268 to 293, the export_user_data method
has two critical issues: it constructs the SQL query using string interpolation,
causing SQL injection risk, and it returns the user's password in the output,
exposing sensitive data. Fix this by using parameterized queries with
placeholders to safely pass user_id to the SQL statement, and remove the
password field from the returned dictionary to avoid exposing it.
| def import_users_from_csv(self, csv_file_path: str) -> bool: | ||
| """Import users from CSV file""" | ||
| try: | ||
| import csv | ||
| conn = sqlite3.connect(self.db_path) | ||
| cursor = conn.cursor() | ||
|
|
||
| with open(csv_file_path, 'r') as file: | ||
| reader = csv.DictReader(file) | ||
| for row in reader: | ||
| # SECURITY ISSUE 18: SQL injection in CSV import | ||
| query = f""" | ||
| INSERT INTO users (username, password, email, role) | ||
| VALUES ('{row['username']}', '{row['password']}', '{row['email']}', '{row['role']}') | ||
| """ | ||
| cursor.execute(query) | ||
|
|
||
| conn.commit() | ||
| conn.close() | ||
| return True | ||
| except Exception as e: | ||
| logging.error(f"Error importing users: {e}") | ||
| return False |
There was a problem hiding this comment.
Critical: SQL injection in import_users_from_csv
CSV import has SQL injection vulnerability and doesn't hash passwords.
def import_users_from_csv(self, csv_file_path: str) -> bool:
"""Import users from CSV file"""
try:
import csv
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
with open(csv_file_path, 'r') as file:
reader = csv.DictReader(file)
for row in reader:
- # SECURITY ISSUE 18: SQL injection in CSV import
- query = f"""
- INSERT INTO users (username, password, email, role)
- VALUES ('{row['username']}', '{row['password']}', '{row['email']}', '{row['role']}')
- """
- cursor.execute(query)
+ # Validate and hash password
+ if not self.validate_password(row.get('password', '')):
+ logging.warning(f"Skipping user {row.get('username')} - weak password")
+ continue
+
+ hashed_password = self.hash_password(row['password'])
+
+ # Use parameterized query
+ cursor.execute("""
+ INSERT OR IGNORE INTO users (username, password, email, role)
+ VALUES (?, ?, ?, ?)
+ """, (row['username'], hashed_password, row.get('email'), row.get('role', 'user')))
conn.commit()
conn.close()
return TrueCommittable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In test_user_management.py around lines 295 to 317, the import_users_from_csv
method constructs SQL queries by directly embedding CSV values, causing SQL
injection risks and storing passwords in plain text. To fix this, replace the
string interpolation with parameterized queries using placeholders and pass the
CSV values as parameters to cursor.execute. Additionally, hash the passwords
before inserting them into the database to enhance security.
| def get_system_stats(self) -> Dict: | ||
| """Get system statistics""" | ||
| try: | ||
| conn = sqlite3.connect(self.db_path) | ||
| cursor = conn.cursor() | ||
|
|
||
| # SECURITY ISSUE 19: SQL injection in stats query | ||
| query = "SELECT COUNT(*) FROM users" | ||
| cursor.execute(query) | ||
| total_users = cursor.fetchone()[0] | ||
|
|
||
| query = "SELECT COUNT(*) FROM users WHERE role = 'admin'" | ||
| cursor.execute(query) | ||
| admin_users = cursor.fetchone()[0] | ||
|
|
||
| conn.close() | ||
|
|
||
| return { | ||
| 'total_users': total_users, | ||
| 'admin_users': admin_users, | ||
| 'regular_users': total_users - admin_users, | ||
| 'database_path': self.db_path, # SECURITY ISSUE 20: Exposing internal paths | ||
| 'secret_key': SECRET_KEY # SECURITY ISSUE 21: Exposing secret key | ||
| } | ||
| except Exception as e: | ||
| logging.error(f"Error getting stats: {e}") | ||
| return {} |
There was a problem hiding this comment.
Critical: Exposure of sensitive information in get_system_stats
This method exposes the database path and secret key, which are sensitive internal details.
def get_system_stats(self) -> Dict:
"""Get system statistics"""
try:
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
- # SECURITY ISSUE 19: SQL injection in stats query
query = "SELECT COUNT(*) FROM users"
cursor.execute(query)
total_users = cursor.fetchone()[0]
query = "SELECT COUNT(*) FROM users WHERE role = 'admin'"
cursor.execute(query)
admin_users = cursor.fetchone()[0]
conn.close()
return {
'total_users': total_users,
'admin_users': admin_users,
'regular_users': total_users - admin_users,
- 'database_path': self.db_path, # SECURITY ISSUE 20: Exposing internal paths
- 'secret_key': SECRET_KEY # SECURITY ISSUE 21: Exposing secret key
+ # Never expose internal paths or secrets
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def get_system_stats(self) -> Dict: | |
| """Get system statistics""" | |
| try: | |
| conn = sqlite3.connect(self.db_path) | |
| cursor = conn.cursor() | |
| # SECURITY ISSUE 19: SQL injection in stats query | |
| query = "SELECT COUNT(*) FROM users" | |
| cursor.execute(query) | |
| total_users = cursor.fetchone()[0] | |
| query = "SELECT COUNT(*) FROM users WHERE role = 'admin'" | |
| cursor.execute(query) | |
| admin_users = cursor.fetchone()[0] | |
| conn.close() | |
| return { | |
| 'total_users': total_users, | |
| 'admin_users': admin_users, | |
| 'regular_users': total_users - admin_users, | |
| 'database_path': self.db_path, # SECURITY ISSUE 20: Exposing internal paths | |
| 'secret_key': SECRET_KEY # SECURITY ISSUE 21: Exposing secret key | |
| } | |
| except Exception as e: | |
| logging.error(f"Error getting stats: {e}") | |
| return {} | |
| def get_system_stats(self) -> Dict: | |
| """Get system statistics""" | |
| try: | |
| conn = sqlite3.connect(self.db_path) | |
| cursor = conn.cursor() | |
| query = "SELECT COUNT(*) FROM users" | |
| cursor.execute(query) | |
| total_users = cursor.fetchone()[0] | |
| query = "SELECT COUNT(*) FROM users WHERE role = 'admin'" | |
| cursor.execute(query) | |
| admin_users = cursor.fetchone()[0] | |
| conn.close() | |
| return { | |
| 'total_users': total_users, | |
| 'admin_users': admin_users, | |
| 'regular_users': total_users - admin_users, | |
| # Never expose internal paths or secrets | |
| } | |
| except Exception as e: | |
| logging.error(f"Error getting stats: {e}") | |
| return {} |
🤖 Prompt for AI Agents
In test_user_management.py around lines 319 to 345, the get_system_stats method
exposes sensitive information by returning the database path and secret key. To
fix this, remove 'database_path' and 'secret_key' from the returned dictionary
so that these internal details are not exposed outside the method.
| def create_user_endpoint(username: str, password: str, email: str = None): | ||
| """API endpoint for creating users""" | ||
| # SECURITY ISSUE 22: No input validation | ||
| return user_manager.create_user(username, password, email) | ||
|
|
||
| def login_endpoint(username: str, password: str): | ||
| """API endpoint for user login""" | ||
| # SECURITY ISSUE 23: No input sanitization | ||
| return user_manager.authenticate_user(username, password) | ||
|
|
||
| def get_user_endpoint(user_id: str): | ||
| """API endpoint for getting user by ID""" | ||
| # SECURITY ISSUE 24: No type validation | ||
| return user_manager.get_user_by_id(int(user_id)) | ||
|
|
||
| def search_users_endpoint(search_term: str): | ||
| """API endpoint for searching users""" | ||
| # SECURITY ISSUE 25: No input sanitization | ||
| return user_manager.search_users(search_term) |
There was a problem hiding this comment.
Add input validation to API endpoints
All API endpoints lack input validation and sanitization, making them vulnerable to various attacks.
+import re
+from typing import Union
+
def create_user_endpoint(username: str, password: str, email: str = None):
"""API endpoint for creating users"""
- # SECURITY ISSUE 22: No input validation
+ # Validate username (alphanumeric and underscore only)
+ if not re.match(r'^[a-zA-Z0-9_]{3,20}$', username):
+ return {'error': 'Invalid username format'}
+
+ # Validate email if provided
+ if email and not re.match(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$', email):
+ return {'error': 'Invalid email format'}
+
return user_manager.create_user(username, password, email)
def login_endpoint(username: str, password: str):
"""API endpoint for user login"""
- # SECURITY ISSUE 23: No input sanitization
+ # Add rate limiting here to prevent brute force attacks
+ # Validate input length to prevent DoS
+ if len(username) > 100 or len(password) > 100:
+ return {'error': 'Input too long'}
+
return user_manager.authenticate_user(username, password)
-def get_user_endpoint(user_id: str):
+def get_user_endpoint(user_id: Union[str, int]):
"""API endpoint for getting user by ID"""
- # SECURITY ISSUE 24: No type validation
- return user_manager.get_user_by_id(int(user_id))
+ # Validate user_id is a positive integer
+ try:
+ user_id = int(user_id)
+ if user_id <= 0:
+ return {'error': 'Invalid user ID'}
+ except (ValueError, TypeError):
+ return {'error': 'User ID must be a number'}
+
+ return user_manager.get_user_by_id(user_id)
def search_users_endpoint(search_term: str):
"""API endpoint for searching users"""
- # SECURITY ISSUE 25: No input sanitization
+ # Limit search term length to prevent DoS
+ if len(search_term) > 50:
+ return {'error': 'Search term too long'}
+
+ # Remove special characters that might be used for injection
+ search_term = re.sub(r'[^\w\[email protected]]', '', search_term)
+
return user_manager.search_users(search_term)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def create_user_endpoint(username: str, password: str, email: str = None): | |
| """API endpoint for creating users""" | |
| # SECURITY ISSUE 22: No input validation | |
| return user_manager.create_user(username, password, email) | |
| def login_endpoint(username: str, password: str): | |
| """API endpoint for user login""" | |
| # SECURITY ISSUE 23: No input sanitization | |
| return user_manager.authenticate_user(username, password) | |
| def get_user_endpoint(user_id: str): | |
| """API endpoint for getting user by ID""" | |
| # SECURITY ISSUE 24: No type validation | |
| return user_manager.get_user_by_id(int(user_id)) | |
| def search_users_endpoint(search_term: str): | |
| """API endpoint for searching users""" | |
| # SECURITY ISSUE 25: No input sanitization | |
| return user_manager.search_users(search_term) | |
| import re | |
| from typing import Union | |
| def create_user_endpoint(username: str, password: str, email: str = None): | |
| """API endpoint for creating users""" | |
| # Validate username (alphanumeric and underscore only) | |
| if not re.match(r'^[a-zA-Z0-9_]{3,20}$', username): | |
| return {'error': 'Invalid username format'} | |
| # Validate email if provided | |
| if email and not re.match(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$', email): | |
| return {'error': 'Invalid email format'} | |
| return user_manager.create_user(username, password, email) | |
| def login_endpoint(username: str, password: str): | |
| """API endpoint for user login""" | |
| # Add rate limiting here to prevent brute force attacks | |
| # Validate input length to prevent DoS | |
| if len(username) > 100 or len(password) > 100: | |
| return {'error': 'Input too long'} | |
| return user_manager.authenticate_user(username, password) | |
| def get_user_endpoint(user_id: Union[str, int]): | |
| """API endpoint for getting user by ID""" | |
| # Validate user_id is a positive integer | |
| try: | |
| user_id = int(user_id) | |
| if user_id <= 0: | |
| return {'error': 'Invalid user ID'} | |
| except (ValueError, TypeError): | |
| return {'error': 'User ID must be a number'} | |
| return user_manager.get_user_by_id(user_id) | |
| def search_users_endpoint(search_term: str): | |
| """API endpoint for searching users""" | |
| # Limit search term length to prevent DoS | |
| if len(search_term) > 50: | |
| return {'error': 'Search term too long'} | |
| # Remove special characters that might be used for injection | |
| search_term = re.sub(r'[^\w\[email protected]]', '', search_term) | |
| return user_manager.search_users(search_term) |
🤖 Prompt for AI Agents
In test_user_management.py around lines 350 to 368, the API endpoints lack input
validation and sanitization, exposing security risks. Add proper input
validation for all parameters, ensuring usernames, passwords, emails, user IDs,
and search terms conform to expected formats and types. Implement sanitization
to prevent injection attacks by cleaning inputs before passing them to
user_manager methods.
| if __name__ == "__main__": | ||
| # Create some test users | ||
| user_manager.create_user("john_doe", "password123", "[email protected]") | ||
| user_manager.create_user("jane_smith", "password456", "[email protected]") | ||
| user_manager.create_user("bob_wilson", "password789", "[email protected]") | ||
|
|
||
| # Test authentication | ||
| user = user_manager.authenticate_user("john_doe", "password123") | ||
| print(f"Authenticated user: {user}") | ||
|
|
||
| # Test search | ||
| users = user_manager.search_users("john") | ||
| print(f"Search results: {users}") | ||
|
|
||
| # Test permissions | ||
| permissions = user_manager.get_user_permissions(1) | ||
| print(f"User permissions: {permissions}") | ||
|
|
||
| # Test system stats | ||
| stats = user_manager.get_system_stats() | ||
| print(f"System stats: {stats}") No newline at end of file |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve example usage with better practices
The example usage should demonstrate secure practices, not perpetuate bad patterns.
# Example usage
if __name__ == "__main__":
- # Create some test users
- user_manager.create_user("john_doe", "password123", "[email protected]")
- user_manager.create_user("jane_smith", "password456", "[email protected]")
- user_manager.create_user("bob_wilson", "password789", "[email protected]")
+ # Example: Create users with strong passwords
+ # In production, passwords should come from user input, not hardcoded
+ import getpass
+
+ # Example of secure password input
+ # password = getpass.getpass("Enter password: ")
+
+ # For demonstration only - use strong passwords
+ user_manager.create_user("john_doe", "J0hn!D0e@2024#Secure", "[email protected]")
# Test authentication
- user = user_manager.authenticate_user("john_doe", "password123")
- print(f"Authenticated user: {user}")
+ user = user_manager.authenticate_user("john_doe", "J0hn!D0e@2024#Secure")
+ if user:
+ print(f"Authentication successful for user ID: {user['id']}")
+ else:
+ print("Authentication failed")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if __name__ == "__main__": | |
| # Create some test users | |
| user_manager.create_user("john_doe", "password123", "[email protected]") | |
| user_manager.create_user("jane_smith", "password456", "[email protected]") | |
| user_manager.create_user("bob_wilson", "password789", "[email protected]") | |
| # Test authentication | |
| user = user_manager.authenticate_user("john_doe", "password123") | |
| print(f"Authenticated user: {user}") | |
| # Test search | |
| users = user_manager.search_users("john") | |
| print(f"Search results: {users}") | |
| # Test permissions | |
| permissions = user_manager.get_user_permissions(1) | |
| print(f"User permissions: {permissions}") | |
| # Test system stats | |
| stats = user_manager.get_system_stats() | |
| print(f"System stats: {stats}") | |
| # Example usage | |
| if __name__ == "__main__": | |
| # Example: Create users with strong passwords | |
| # In production, passwords should come from user input, not hardcoded | |
| import getpass | |
| # Example of secure password input | |
| # password = getpass.getpass("Enter password: ") | |
| # For demonstration only - use strong passwords | |
| user_manager.create_user("john_doe", "J0hn!D0e@2024#Secure", "[email protected]") | |
| # Test authentication | |
| user = user_manager.authenticate_user("john_doe", "J0hn!D0e@2024#Secure") | |
| if user: | |
| print(f"Authentication successful for user ID: {user['id']}") | |
| else: | |
| print("Authentication failed") | |
| # Test search | |
| users = user_manager.search_users("john") | |
| print(f"Search results: {users}") | |
| # Test permissions | |
| permissions = user_manager.get_user_permissions(1) | |
| print(f"User permissions: {permissions}") | |
| # Test system stats | |
| stats = user_manager.get_system_stats() | |
| print(f"System stats: {stats}") |
🤖 Prompt for AI Agents
In test_user_management.py around lines 371 to 391, the example usage shows
creating users with plaintext passwords and printing sensitive information
directly. Update the example to avoid hardcoding passwords in plaintext, use
environment variables or secure input methods for passwords, and avoid printing
sensitive user details or authentication tokens. Instead, demonstrate secure
handling by showing success/failure messages without exposing credentials or
sensitive data.
|
/refacto-test |
|
Refacto is reviewing this PR. Please wait for the review comments to be posted. |
Code Review: User Management Security👍 Well Done
📌 Files Processed
📝 Additional Comments
|
| query = f""" | ||
| INSERT INTO users (username, password, email, role) | ||
| VALUES ('{username}', '{password}', '{email}', '{role}') | ||
| """ |
There was a problem hiding this comment.
SQL Injection Vulnerabilities
String interpolation in SQL queries enables SQL injection attacks. Malicious input could execute arbitrary SQL commands, causing data corruption or unauthorized access.
| query = f""" | |
| INSERT INTO users (username, password, email, role) | |
| VALUES ('{username}', '{password}', '{email}', '{role}') | |
| """ | |
| query = """ | |
| INSERT INTO users (username, password, email, role) | |
| VALUES (?, ?, ?, ?) | |
| """ | |
| cursor.execute(query, (username, password, email, role)) |
Standards
- OWASP-A03
- CWE-89
| """Hash password using MD5 (SECURITY ISSUE 10: Weak hashing)""" | ||
| return hashlib.md5(password.encode()).hexdigest() | ||
|
|
There was a problem hiding this comment.
Weak Password Hashing
MD5 is cryptographically broken and unsuitable for password hashing. It lacks salt and is vulnerable to rainbow table attacks, compromising account security.
| """Hash password using MD5 (SECURITY ISSUE 10: Weak hashing)""" | |
| return hashlib.md5(password.encode()).hexdigest() | |
| def hash_password(self, password: str) -> str: | |
| """Hash password using strong algorithm with salt""" | |
| import bcrypt | |
| salt = bcrypt.gensalt() | |
| return bcrypt.hashpw(password.encode(), salt).decode('utf-8') |
Standards
- OWASP-A02
- CWE-328
| command = f"cp {self.db_path} {backup_path}" | ||
| subprocess.run(command, shell=True, check=True) | ||
| return True |
There was a problem hiding this comment.
Command Injection Risk
Direct string interpolation in shell commands enables command injection. Malicious input in backup_path could execute arbitrary system commands, compromising system integrity.
| command = f"cp {self.db_path} {backup_path}" | |
| subprocess.run(command, shell=True, check=True) | |
| return True | |
| import subprocess | |
| import shutil | |
| # Safer approach using shutil | |
| shutil.copy2(self.db_path, backup_path) | |
| # Alternative using subprocess without shell=True | |
| # subprocess.run(["cp", self.db_path, backup_path], check=True) |
Standards
- OWASP-A03
- CWE-78
| # SECURITY ISSUE 1: Hardcoded database credentials and connection string | ||
| DATABASE_URL = "sqlite:///users.db" | ||
| ADMIN_USERNAME = "admin" | ||
| ADMIN_PASSWORD = "admin123" # SECURITY ISSUE: Hardcoded weak password | ||
| SECRET_KEY = "my-super-secret-key-123" # SECURITY ISSUE: Hardcoded secret key |
There was a problem hiding this comment.
Hardcoded Credentials
Hardcoded credentials in source code pose security risk. Credentials in version control are accessible to anyone with repository access, compromising system security.
| # SECURITY ISSUE 1: Hardcoded database credentials and connection string | |
| DATABASE_URL = "sqlite:///users.db" | |
| ADMIN_USERNAME = "admin" | |
| ADMIN_PASSWORD = "admin123" # SECURITY ISSUE: Hardcoded weak password | |
| SECRET_KEY = "my-super-secret-key-123" # SECURITY ISSUE: Hardcoded secret key | |
| import os | |
| # Load from environment variables with defaults for development only | |
| DATABASE_URL = os.environ.get("DATABASE_URL", "sqlite:///users.db") | |
| ADMIN_USERNAME = os.environ.get("ADMIN_USERNAME", "admin") | |
| ADMIN_PASSWORD = os.environ.get("ADMIN_PASSWORD", "change-me-in-production") | |
| SECRET_KEY = os.environ.get("SECRET_KEY", "generate-random-key-in-production") |
Standards
- OWASP-A07
- CWE-798
| def validate_password(self, password: str) -> bool: | ||
| """Validate password strength""" | ||
| # SECURITY ISSUE 9: Weak password validation | ||
| if len(password) >= 6: # Too weak minimum length | ||
| return True | ||
| return False |
There was a problem hiding this comment.
Weak Password Validation
Password validation only checks length ≥ 6, allowing weak passwords. Missing complexity requirements (uppercase, lowercase, numbers, symbols) enables brute force attacks and compromises account security.
| def validate_password(self, password: str) -> bool: | |
| """Validate password strength""" | |
| # SECURITY ISSUE 9: Weak password validation | |
| if len(password) >= 6: # Too weak minimum length | |
| return True | |
| return False | |
| def validate_password(self, password: str) -> bool: | |
| """Validate password strength with comprehensive rules""" | |
| if len(password) < 12: | |
| return False | |
| if not re.search(r'[A-Z]', password): | |
| return False | |
| if not re.search(r'[a-z]', password): | |
| return False | |
| if not re.search(r'[0-9]', password): | |
| return False | |
| if not re.search(r'[!@#$%^&*(),.?":{}|<>]', password): | |
| return False | |
| return True |
Standards
- OWASP-A07
- CWE-521
| conn = sqlite3.connect(source) | ||
| df = pd.read_sql_query("SELECT * FROM data", conn) | ||
| conn.close() |
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Resource Leak Risk
Database connection not closed in exception paths. If read_sql_query fails, connection remains open causing resource leaks and potential connection pool exhaustion.
| conn = sqlite3.connect(source) | |
| df = pd.read_sql_query("SELECT * FROM data", conn) | |
| conn.close() | |
| with sqlite3.connect(source) as conn: | |
| df = pd.read_sql_query("SELECT * FROM data", conn) |
Standards
- ISO-IEC-25010-Reliability-Maturity
- DbC-Resource-Management
| def outlier_detection(self, df: pd.DataFrame, method: str = "iqr") -> Dict: | ||
| """Detect outliers using various methods""" | ||
| outliers = {} | ||
| numeric_columns = df.select_dtypes(include=[np.number]).columns | ||
|
|
||
| for col in numeric_columns: | ||
| if method == "iqr": | ||
| Q1 = df[col].quantile(0.25) | ||
| Q3 = df[col].quantile(0.75) | ||
| IQR = Q3 - Q1 | ||
| lower_bound = Q1 - 1.5 * IQR | ||
| upper_bound = Q3 + 1.5 * IQR | ||
| outlier_indices = df[(df[col] < lower_bound) | (df[col] > upper_bound)].index |
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O(n²) Outlier Detection
Outlier detection uses inefficient filtering for each column separately. With large datasets, this creates O(n²) complexity as each column requires full dataframe filtering, causing significant performance degradation.
| def outlier_detection(self, df: pd.DataFrame, method: str = "iqr") -> Dict: | |
| """Detect outliers using various methods""" | |
| outliers = {} | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns | |
| for col in numeric_columns: | |
| if method == "iqr": | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| outlier_indices = df[(df[col] < lower_bound) | (df[col] > upper_bound)].index | |
| def outlier_detection(self, df: pd.DataFrame, method: str = "iqr") -> Dict: | |
| """Detect outliers using various methods""" | |
| outliers = {} | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns | |
| for col in numeric_columns: | |
| if method == "iqr": | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| mask = (df[col] < lower_bound) | (df[col] > upper_bound) | |
| outlier_indices = df.index[mask] | |
| outliers[col] = { | |
| 'count': len(outlier_indices), | |
| 'percentage': len(outlier_indices) / len(df) * 100, | |
| 'indices': outlier_indices.tolist() | |
| } | |
| self.analysis_results['outlier_detection'] = outliers | |
| return outliers |
Standards
- ISO-IEC-25010-Performance-Time-Behaviour
- Algorithm-Opt-Vectorization
| def feature_engineering(self, df: pd.DataFrame) -> pd.DataFrame: | ||
| """Create new features from existing data""" | ||
| # Date features if datetime columns exist | ||
| datetime_columns = df.select_dtypes(include=['datetime64']).columns | ||
| for col in datetime_columns: | ||
| df[f"{col}_year"] = df[col].dt.year | ||
| df[f"{col}_month"] = df[col].dt.month | ||
| df[f"{col}_day"] = df[col].dt.day | ||
| df[f"{col}_dayofweek"] = df[col].dt.dayofweek | ||
| df[f"{col}_quarter"] = df[col].dt.quarter | ||
|
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| # Interaction features for numeric columns | ||
| numeric_columns = df.select_dtypes(include=[np.number]).columns | ||
| if len(numeric_columns) >= 2: | ||
| for i, col1 in enumerate(numeric_columns): | ||
| for col2 in numeric_columns[i+1:]: | ||
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | ||
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) |
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Inefficient Feature Engineering
Feature engineering creates O(n²) complexity with nested loops over numeric columns. For datasets with many numeric columns, this generates excessive features, causing memory spikes and computation bottlenecks.
| def feature_engineering(self, df: pd.DataFrame) -> pd.DataFrame: | |
| """Create new features from existing data""" | |
| # Date features if datetime columns exist | |
| datetime_columns = df.select_dtypes(include=['datetime64']).columns | |
| for col in datetime_columns: | |
| df[f"{col}_year"] = df[col].dt.year | |
| df[f"{col}_month"] = df[col].dt.month | |
| df[f"{col}_day"] = df[col].dt.day | |
| df[f"{col}_dayofweek"] = df[col].dt.dayofweek | |
| df[f"{col}_quarter"] = df[col].dt.quarter | |
| # Interaction features for numeric columns | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns | |
| if len(numeric_columns) >= 2: | |
| for i, col1 in enumerate(numeric_columns): | |
| for col2 in numeric_columns[i+1:]: | |
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | |
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | |
| def feature_engineering(self, df: pd.DataFrame, max_interaction_features: int = 10) -> pd.DataFrame: | |
| """Create new features from existing data""" | |
| # Date features if datetime columns exist | |
| datetime_columns = df.select_dtypes(include=['datetime64']).columns | |
| for col in datetime_columns: | |
| df[f"{col}_year"] = df[col].dt.year | |
| df[f"{col}_month"] = df[col].dt.month | |
| df[f"{col}_day"] = df[col].dt.day | |
| df[f"{col}_dayofweek"] = df[col].dt.dayofweek | |
| df[f"{col}_quarter"] = df[col].dt.quarter | |
| # Interaction features for numeric columns (limited) | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns[:5] # Limit to first 5 columns | |
| feature_count = 0 | |
| if len(numeric_columns) >= 2: | |
| for i, col1 in enumerate(numeric_columns): | |
| for col2 in numeric_columns[i+1:]: | |
| if feature_count >= max_interaction_features: | |
| break | |
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | |
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | |
| feature_count += 2 | |
| if feature_count >= max_interaction_features: | |
| break | |
| return df |
Standards
- ISO-IEC-25010-Performance-Resource-Utilization
- Algorithm-Opt-Complexity-Reduction
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | ||
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| logger.info(f"Cleaned data: {len(df)} rows remaining") | ||
| return df | ||
|
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| def transform_data(self, df: pd.DataFrame, transformations: List[Dict]) -> pd.DataFrame: | ||
| """Apply various data transformations""" |
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Exception Handling Gap
No exception handling for columns with non-numeric data or NaN values. Quantile operations on invalid data will cause runtime errors, crashing the data cleaning process.
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| logger.info(f"Cleaned data: {len(df)} rows remaining") | |
| return df | |
| def transform_data(self, df: pd.DataFrame, transformations: List[Dict]) -> pd.DataFrame: | |
| """Apply various data transformations""" | |
| for col in numeric_columns: | |
| try: | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| except Exception as e: | |
| logger.warning(f"Could not remove outliers for column {col}: {e}") |
Standards
- ISO-IEC-25010-Reliability-Fault-Tolerance
- DbC-Error-Handling
|
/refacto-test |
|
PR already reviewed at the latest commit: 897b91a. |
|
/refacto-test |
Code Review: Data Analytics Engine👍 Well Done
📌 Files Processed
📝 Additional Comments
|
| df = pd.read_json(source) | ||
| elif data_type == "sqlite": | ||
| conn = sqlite3.connect(source) | ||
| df = pd.read_sql_query("SELECT * FROM data", conn) |
There was a problem hiding this comment.
SQL Injection Risk
The SQL query uses a hardcoded table name without proper validation. If the table name is later made configurable, it could introduce SQL injection vulnerabilities if not properly parameterized, as string formatting with f-strings for SQL is used elsewhere in the codebase.
| df = pd.read_sql_query("SELECT * FROM data", conn) | |
| def load_data(self, source: str, data_type: str = "csv", table_name: str = "data") -> pd.DataFrame: | |
| """Load data from various sources""" | |
| try: | |
| if data_type == "csv": | |
| df = pd.read_csv(source) | |
| elif data_type == "json": | |
| df = pd.read_json(source) | |
| elif data_type == "sqlite": | |
| conn = sqlite3.connect(source) | |
| # Validate table name to prevent SQL injection | |
| if not re.match(r'^[a-zA-Z0-9_]+$', table_name): | |
| raise ValueError(f"Invalid table name: {table_name}") | |
| df = pd.read_sql_query(f"SELECT * FROM {table_name}", conn) | |
| conn.close() | |
| else: | |
| raise ValueError(f"Unsupported data type: {data_type}") | |
| logger.info(f"Loaded {len(df)} rows from {source}") | |
| return df |
Standards
- CWE-89
- OWASP-A03
| def export_chart(self, fig: go.Figure, filename: str, format: str = "html"): | ||
| """Export chart to various formats""" | ||
| if format == "html": | ||
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | ||
| elif format == "png": | ||
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | ||
| elif format == "pdf": | ||
| fig.write_image(f"{self.config.output_dir}/{filename}.pdf") |
There was a problem hiding this comment.
Path Traversal Risk
The export_chart method doesn't validate the filename parameter, which could lead to path traversal vulnerabilities. An attacker could provide filenames containing '../' to write files outside the intended directory, potentially overwriting system files.
| def export_chart(self, fig: go.Figure, filename: str, format: str = "html"): | |
| """Export chart to various formats""" | |
| if format == "html": | |
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | |
| elif format == "png": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | |
| elif format == "pdf": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.pdf") | |
| def export_chart(self, fig: go.Figure, filename: str, format: str = "html"): | |
| """Export chart to various formats""" | |
| import os | |
| import re | |
| # Create output directory if it doesn't exist | |
| os.makedirs(self.config.output_dir, exist_ok=True) | |
| # Sanitize filename to prevent path traversal | |
| safe_filename = re.sub(r'[^a-zA-Z0-9_-]', '_', os.path.basename(filename)) | |
| if format == "html": | |
| fig.write_html(f"{self.config.output_dir}/{safe_filename}.html") | |
| elif format == "png": | |
| fig.write_image(f"{self.config.output_dir}/{safe_filename}.png") | |
| elif format == "pdf": | |
| fig.write_image(f"{self.config.output_dir}/{safe_filename}.pdf") |
Standards
- CWE-22
- OWASP-A01
| df = pd.read_sql_query("SELECT * FROM data", conn) | ||
| conn.close() | ||
| else: |
There was a problem hiding this comment.
Resource Leak in Database Connection
Database connection isn't closed in exception paths. If read_sql_query fails, the connection remains open, potentially causing resource exhaustion over time as connections accumulate. This can lead to database unavailability.
| df = pd.read_sql_query("SELECT * FROM data", conn) | |
| conn.close() | |
| else: | |
| elif data_type == "sqlite": | |
| with sqlite3.connect(source) as conn: | |
| df = pd.read_sql_query(f"SELECT * FROM {table_name}", conn) |
Standards
- ISO-IEC-25010-Reliability-Resource-Utilization
- SRE-Resource-Management
- DbC-Resource-Cleanup
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | ||
|
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| logger.info(f"Cleaned data: {len(df)} rows remaining") | ||
| return df | ||
|
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| def transform_data(self, df: pd.DataFrame, transformations: List[Dict]) -> pd.DataFrame: |
There was a problem hiding this comment.
Unhandled Exception in Outlier Detection
Quantile operations can fail on columns with non-numeric data or all NaN values, causing unhandled exceptions that crash the entire data cleaning process. This creates a reliability gap in the data processing pipeline.
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| logger.info(f"Cleaned data: {len(df)} rows remaining") | |
| return df | |
| def transform_data(self, df: pd.DataFrame, transformations: List[Dict]) -> pd.DataFrame: | |
| for col in numeric_columns: | |
| try: | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| except Exception as e: | |
| logger.warning(f"Could not remove outliers for column {col}: {e}") |
Standards
- ISO-IEC-25010-Reliability-Fault-Tolerance
- SRE-Error-Handling
- DbC-Defensive-Programming
| """Export chart to various formats""" | ||
| if format == "html": | ||
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | ||
| elif format == "png": | ||
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | ||
| elif format == "pdf": |
There was a problem hiding this comment.
Missing Directory Creation Before File Export
The export_chart method attempts to write files without ensuring the output directory exists. If the directory doesn't exist, file operations will fail with FileNotFoundError, causing chart export to silently fail.
| """Export chart to various formats""" | |
| if format == "html": | |
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | |
| elif format == "png": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | |
| elif format == "pdf": | |
| def export_chart(self, fig: go.Figure, filename: str, format: str = "html"): | |
| """Export chart to various formats""" | |
| import os | |
| os.makedirs(self.config.output_dir, exist_ok=True) | |
| if format == "html": | |
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | |
| elif format == "png": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | |
| elif format == "pdf": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.pdf") |
Standards
- ISO-IEC-25010-Reliability-Fault-Tolerance
- SRE-Error-Handling
- DbC-Precondition-Validation
| if len(numeric_columns) >= 2: | ||
| for i, col1 in enumerate(numeric_columns): | ||
| for col2 in numeric_columns[i+1:]: | ||
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | ||
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | ||
|
|
||
| return df |
There was a problem hiding this comment.
O(n²) Complexity in Feature Engineering
The nested loop creates O(n²) feature combinations where n is the number of numeric columns. For datasets with many numeric columns, this generates excessive features causing memory spikes and computation bottlenecks.
| if len(numeric_columns) >= 2: | |
| for i, col1 in enumerate(numeric_columns): | |
| for col2 in numeric_columns[i+1:]: | |
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | |
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | |
| return df | |
| # Interaction features for numeric columns (limited) | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns | |
| max_interaction_features = 10 # Limit total interaction features | |
| feature_count = 0 | |
| if len(numeric_columns) >= 2: | |
| for i, col1 in enumerate(numeric_columns): | |
| for col2 in numeric_columns[i+1:]: | |
| if feature_count >= max_interaction_features: | |
| break | |
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | |
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | |
| feature_count += 2 | |
| if feature_count >= max_interaction_features: | |
| break |
Standards
- ISO-IEC-25010-Performance-Time-Behaviour
- Algorithm-Opt-Complexity-Reduction
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | ||
|
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||
| logger.info(f"Cleaned data: {len(df)} rows remaining") | ||
| return df |
There was a problem hiding this comment.
Outlier Removal Logic
The outlier removal logic modifies the dataframe in-place during iteration, which can lead to incorrect results as the dataframe's size changes during the loop. This creates a logical inconsistency where outliers in later columns are evaluated against an already filtered dataset.
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| logger.info(f"Cleaned data: {len(df)} rows remaining") | |
| return df | |
| def clean_data(self, df: pd.DataFrame, remove_outliers: bool = False) -> pd.DataFrame: | |
| """Clean and preprocess data""" | |
| if df.empty: | |
| return df | |
| # Remove duplicates | |
| df = df.drop_duplicates() | |
| # Handle missing values | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns | |
| categorical_columns = df.select_dtypes(include=['object']).columns | |
| # Fill numeric missing values with median | |
| for col in numeric_columns: | |
| if df[col].isnull().sum() > 0: | |
| df[col].fillna(df[col].median(), inplace=True) | |
| # Fill categorical missing values with mode | |
| for col in categorical_columns: | |
| if df[col].isnull().sum() > 0: | |
| df[col].fillna(df[col].mode()[0], inplace=True) | |
| # Remove outliers using IQR method for numeric columns | |
| if remove_outliers: | |
| mask = pd.Series(True, index=df.index) | |
| for col in numeric_columns: | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| col_mask = (df[col] >= lower_bound) & (df[col] <= upper_bound) | |
| mask &= col_mask | |
| df = df[mask] | |
| logger.info(f"Cleaned data: {len(df)} rows remaining") | |
| return df |
Standards
- Algorithm-Correctness-Data-Filtering
- Mathematical-Accuracy-Statistical-Operations
| for i, col1 in enumerate(numeric_columns): | ||
| for col2 in numeric_columns[i+1:]: | ||
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | ||
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) |
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Division By Zero
The code adds a small epsilon (1e-8) to prevent division by zero, but this approach can produce misleading results when col2 contains legitimate zeros. This creates incorrect ratio features that may lead to incorrect analytical conclusions.
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | |
| df[f"{col1}_{col2}_ratio"] = np.divide(df[col1], df[col2], out=np.zeros_like(df[col1]), where=df[col2]!=0) |
Standards
- Mathematical-Accuracy-Division-Safety
- Algorithm-Correctness-Numerical-Stability
| from dataclasses import dataclass | ||
| from abc import ABC, abstractmethod | ||
| import warnings | ||
| warnings.filterwarnings('ignore') |
There was a problem hiding this comment.
Unsafe Warning Suppression
Globally suppressing all warnings can hide important security issues, deprecation notices, or data integrity problems. This makes it harder to identify potential vulnerabilities and bugs during development and in production.
| warnings.filterwarnings('ignore') | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Instead of suppressing all warnings, handle specific ones where needed | |
| # For example: | |
| # import warnings | |
| # warnings.filterwarnings('ignore', category=FutureWarning, module='pandas') |
Standards
- CWE-778
- OWASP-A04
| report.append(f"### {col}") | ||
| report.append(f"- Mean: {stats['mean'].get(col, 'N/A'):.2f}") | ||
| report.append(f"- Median: {stats['median'].get(col, 'N/A'):.2f}") |
There was a problem hiding this comment.
Potential Type Error
Using 'N/A' as a default value for numeric formatting will cause TypeError when formatting with :.2f. This could crash the report generation process, potentially leading to denial of service or information disclosure through error messages.
| report.append(f"### {col}") | |
| report.append(f"- Mean: {stats['mean'].get(col, 'N/A'):.2f}") | |
| report.append(f"- Median: {stats['median'].get(col, 'N/A'):.2f}") | |
| report.append(f"### {col}") | |
| mean_val = stats['mean'].get(col) | |
| median_val = stats['median'].get(col) | |
| std_val = stats['std'].get(col) | |
| report.append(f"- Mean: {mean_val:.2f}" if mean_val is not None else "- Mean: N/A") | |
| report.append(f"- Median: {median_val:.2f}" if median_val is not None else "- Median: N/A") | |
| report.append(f"- Std Dev: {std_val:.2f}" if std_val is not None else "- Std Dev: N/A") | |
| report.append("") |
Standards
- CWE-703
- OWASP-A04
|
/refacto-test |
Code Review: Data Analytics Engine Reliability and Security👍 Well Done
📌 Files Processed
📝 Additional Comments
|
| """Export chart to various formats""" | ||
| if format == "html": | ||
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | ||
| elif format == "png": | ||
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | ||
| elif format == "pdf": | ||
| fig.write_image(f"{self.config.output_dir}/{filename}.pdf") | ||
|
|
There was a problem hiding this comment.
Path Traversal Risk in Chart Export
The export_chart method doesn't validate the filename parameter, allowing path traversal attacks. An attacker could provide filenames with '../' sequences to write files outside the intended directory, potentially overwriting system files or accessing sensitive information.
| """Export chart to various formats""" | |
| if format == "html": | |
| fig.write_html(f"{self.config.output_dir}/{filename}.html") | |
| elif format == "png": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.png") | |
| elif format == "pdf": | |
| fig.write_image(f"{self.config.output_dir}/{filename}.pdf") | |
| def export_chart(self, fig: go.Figure, filename: str, format: str = "html"): | |
| """Export chart to various formats""" | |
| import os | |
| import re | |
| # Create output directory if it doesn't exist | |
| os.makedirs(self.config.output_dir, exist_ok=True) | |
| # Sanitize filename to prevent path traversal | |
| safe_filename = re.sub(r'[^a-zA-Z0-9_-]', '_', os.path.basename(filename)) | |
| if format == "html": | |
| fig.write_html(f"{self.config.output_dir}/{safe_filename}.html") | |
| elif format == "png": | |
| fig.write_image(f"{self.config.output_dir}/{safe_filename}.png") | |
| elif format == "pdf": | |
| fig.write_image(f"{self.config.output_dir}/{safe_filename}.pdf") |
Standards
- CWE-22
- OWASP-A01
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | ||
|
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| logger.info(f"Cleaned data: {len(df)} rows remaining") | ||
| return df | ||
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| def transform_data(self, df: pd.DataFrame, transformations: List[Dict]) -> pd.DataFrame: | ||
| """Apply various data transformations""" | ||
| for transform in transformations: |
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Unhandled Exception in Outlier Detection
Quantile operations can fail on columns with non-numeric data or all NaN values, causing unhandled exceptions that crash the entire data cleaning process. This creates a reliability gap in the data processing pipeline.
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| logger.info(f"Cleaned data: {len(df)} rows remaining") | |
| return df | |
| def transform_data(self, df: pd.DataFrame, transformations: List[Dict]) -> pd.DataFrame: | |
| """Apply various data transformations""" | |
| for transform in transformations: | |
| # Remove outliers using IQR method for numeric columns | |
| for col in numeric_columns: | |
| try: | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| except Exception as e: | |
| logger.warning(f"Could not remove outliers for column {col}: {e}") |
Standards
- ISO-IEC-25010-Reliability-Fault-Tolerance
- SRE-Error-Handling
- DbC-Defensive-Programming
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | ||
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| logger.info(f"Cleaned data: {len(df)} rows remaining") | ||
| return df | ||
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| def transform_data(self, df: pd.DataFrame, transformations: List[Dict]) -> pd.DataFrame: |
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Memory Leak in DataFrame Operations
The outlier removal logic creates a new DataFrame for each column processed, causing excessive memory allocation. For large datasets with many columns, this leads to significant memory spikes and potential out-of-memory errors as each iteration allocates a new DataFrame.
| df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)] | |
| logger.info(f"Cleaned data: {len(df)} rows remaining") | |
| return df | |
| def transform_data(self, df: pd.DataFrame, transformations: List[Dict]) -> pd.DataFrame: | |
| # Remove outliers using IQR method for numeric columns | |
| if df.empty: | |
| return df | |
| # Create a single mask for all columns | |
| mask = pd.Series(True, index=df.index) | |
| for col in numeric_columns: | |
| try: | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower_bound = Q1 - 1.5 * IQR | |
| upper_bound = Q3 + 1.5 * IQR | |
| col_mask = (df[col] >= lower_bound) & (df[col] <= upper_bound) | |
| mask &= col_mask | |
| except Exception as e: | |
| logger.warning(f"Could not process outliers for column {col}: {e}") | |
| # Apply the combined mask once | |
| df = df[mask] |
Standards
- ISO-IEC-25010-Performance-Resource-Utilization
- Algorithm-Opt-Batch-Loading
| if df[col].isnull().sum() > 0: | ||
| df[col].fillna(df[col].mode()[0], inplace=True) | ||
|
|
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Mode Access Error in Categorical Data Cleaning
The code assumes df[col].mode() always returns a non-empty Series, but mode() can return an empty Series if all values are NaN or there's no unique mode. This will cause an IndexError when accessing index [0], breaking the data cleaning process.
| if df[col].isnull().sum() > 0: | |
| df[col].fillna(df[col].mode()[0], inplace=True) | |
| for col in categorical_columns: | |
| if df[col].isnull().sum() > 0: | |
| mode_values = df[col].mode() | |
| if not mode_values.empty: | |
| df[col].fillna(mode_values[0], inplace=True) |
Standards
- Algorithm-Correctness-Data-Processing
- Mathematical-Accuracy-Statistical-Operations
| conn = sqlite3.connect(source) | ||
| df = pd.read_sql_query("SELECT * FROM data", conn) | ||
| conn.close() | ||
| else: |
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Resource Leak in Database Connection
Database connection isn't closed in exception paths. If read_sql_query fails, the connection remains open, potentially causing resource exhaustion over time as connections accumulate, leading to database unavailability.
| conn = sqlite3.connect(source) | |
| df = pd.read_sql_query("SELECT * FROM data", conn) | |
| conn.close() | |
| else: | |
| elif data_type == "sqlite": | |
| with sqlite3.connect(source) as conn: | |
| df = pd.read_sql_query("SELECT * FROM data", conn) |
Standards
- ISO-IEC-25010-Reliability-Resource-Utilization
- SRE-Resource-Management
- DbC-Resource-Cleanup
| for i, col1 in enumerate(numeric_columns): | ||
| for col2 in numeric_columns[i+1:]: | ||
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | ||
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | ||
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| return df | ||
|
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Unbounded Feature Engineering Complexity
The feature engineering creates O(n²) feature combinations where n is the number of numeric columns. With many numeric columns, this generates excessive features causing memory spikes and performance degradation. A dataset with 100 numeric columns would create 9,900 new columns.
| for i, col1 in enumerate(numeric_columns): | |
| for col2 in numeric_columns[i+1:]: | |
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | |
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | |
| return df | |
| # Interaction features for numeric columns (limited) | |
| numeric_columns = df.select_dtypes(include=[np.number]).columns | |
| max_interaction_features = 10 # Limit total interaction features | |
| feature_count = 0 | |
| if len(numeric_columns) >= 2: | |
| for i, col1 in enumerate(numeric_columns): | |
| for col2 in numeric_columns[i+1:]: | |
| if feature_count >= max_interaction_features: | |
| break | |
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | |
| df[f"{col1}_{col2}_ratio"] = np.divide(df[col1], df[col2], out=np.zeros_like(df[col1]), where=df[col2]!=0) | |
| feature_count += 2 | |
| if feature_count >= max_interaction_features: | |
| break |
Standards
- ISO-IEC-25010-Performance-Time-Behaviour
- Algorithm-Opt-Complexity-Reduction
| report.append(f"### {col}") | ||
| report.append(f"- Mean: {stats['mean'].get(col, 'N/A'):.2f}") | ||
| report.append(f"- Median: {stats['median'].get(col, 'N/A'):.2f}") | ||
| report.append(f"- Std Dev: {stats['std'].get(col, 'N/A'):.2f}") | ||
| report.append("") |
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Statistical Formatting Error
Using 'N/A' as default for numeric values will cause TypeError when formatting with :.2f since 'N/A' is a string. This will crash the report generation process when a column is missing from the statistics dictionary.
| report.append(f"### {col}") | |
| report.append(f"- Mean: {stats['mean'].get(col, 'N/A'):.2f}") | |
| report.append(f"- Median: {stats['median'].get(col, 'N/A'):.2f}") | |
| report.append(f"- Std Dev: {stats['std'].get(col, 'N/A'):.2f}") | |
| report.append("") | |
| report.append(f"### {col}") | |
| mean_val = stats['mean'].get(col) | |
| median_val = stats['median'].get(col) | |
| std_val = stats['std'].get(col) | |
| report.append(f"- Mean: {mean_val:.2f}" if mean_val is not None else "- Mean: N/A") | |
| report.append(f"- Median: {median_val:.2f}" if median_val is not None else "- Median: N/A") | |
| report.append(f"- Std Dev: {std_val:.2f}" if std_val is not None else "- Std Dev: N/A") | |
| report.append("") |
Standards
- Algorithm-Correctness-Type-Safety
- Business-Rule-Error-Handling
| conn = sqlite3.connect(source) | ||
| df = pd.read_sql_query("SELECT * FROM data", conn) | ||
| conn.close() | ||
| else: |
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SQL Injection Risk in Database Query
The SQL query uses a hardcoded table name. If this becomes configurable, it could introduce SQL injection vulnerabilities if not properly parameterized, especially since string formatting with f-strings for SQL is used elsewhere in the codebase.
| conn = sqlite3.connect(source) | |
| df = pd.read_sql_query("SELECT * FROM data", conn) | |
| conn.close() | |
| else: | |
| def load_data(self, source: str, data_type: str = "csv", table_name: str = "data") -> pd.DataFrame: | |
| """Load data from various sources""" | |
| try: | |
| if data_type == "csv": | |
| df = pd.read_csv(source) | |
| elif data_type == "json": | |
| df = pd.read_json(source) | |
| elif data_type == "sqlite": | |
| conn = sqlite3.connect(source) | |
| # Validate table name to prevent SQL injection | |
| if not re.match(r'^[a-zA-Z0-9_]+$', table_name): | |
| raise ValueError(f"Invalid table name: {table_name}") | |
| df = pd.read_sql_query(f"SELECT * FROM {table_name}", conn) | |
| conn.close() | |
| else: | |
| raise ValueError(f"Unsupported data type: {data_type}") | |
| logger.info(f"Loaded {len(df)} rows from {source}") | |
| return df |
Standards
- CWE-89
- OWASP-A03
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | ||
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) |
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Unsafe Division Operation
Adding a small epsilon (1e-8) to prevent division by zero can produce misleading results when col2 contains legitimate zeros. This creates incorrect ratio features that may lead to incorrect analytical conclusions.
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | |
| df[f"{col1}_{col2}_ratio"] = df[col1] / (df[col2] + 1e-8) | |
| df[f"{col1}_{col2}_product"] = df[col1] * df[col2] | |
| df[f"{col1}_{col2}_ratio"] = np.divide(df[col1], df[col2], out=np.zeros_like(df[col1]), where=df[col2]!=0) |
Standards
- Mathematical-Accuracy-Division-Safety
- Algorithm-Correctness-Numerical-Stability
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Summary by CodeRabbit
New Features
Known Issues