Enhanced analysis module with comprehensive metrics integration #16
Conversation
|
@CodiumAI-Agent /review |
|
@sourcery-ai review |
|
/gemini review
|
|
Important Review skippedBot user detected. To trigger a single review, invoke the You can disable this status message by setting the 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
SupportNeed help? Join our Discord community for assistance with any issues or questions. Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
|
/review |
|
/improve |
|
/korbit-review |
|
@codecov-ai-reviewer review |
Reviewer's GuideThis pull request introduces a new File-Level Changes
Tips and commandsInteracting with Sourcery
Customizing Your ExperienceAccess your dashboard to:
Getting Help
|
|
@codegen Implement and upgrade this PR with above Considerations and suggestions from other AI bots |
|
On it! We are reviewing the PR and will provide feedback shortly. |
|
Hey! 👋 I see one of the checks failed. I am on it! 🫡 |
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Code Review
This PR introduces significant enhancements to the analysis module, including the removal of Modal dependencies, improved metrics integration, and new analysis features. The changes aim to provide a more comprehensive and locally runnable analysis tool. Overall, the changes look good, but there are a few areas that could be improved.
Summary of Findings
- Missing Error Handling: The
get_monthly_commitsfunction lacks robust error handling for subprocess execution, potentially leading to unhandled exceptions and inaccurate results. Consider adding more specific error handling for different subprocess errors. - Inconsistent Naming: There are some inconsistencies in naming conventions, such as using snake_case for some variables and camelCase for others. Consistent naming improves readability and maintainability.
- Lack of Input Validation: The
calculate_maintainability_indexfunction does not validate input parameters, which could lead to unexpected behavior or incorrect results. Input validation should be added to ensure the function operates correctly with valid data. - Docstring Improvements: While docstrings have been added, some could be more descriptive, especially for functions like
get_operators_and_operands, where the purpose and usage of the returned values could be clarified.
Merge Readiness
The pull request is not quite ready for merging. While the enhancements are valuable, the identified issues related to error handling, naming consistency, input validation, and docstring clarity should be addressed to ensure the code is robust and maintainable. I am unable to approve this pull request, and recommend that the author address the issues before merging. It is also recommended that others review and approve this code before merging.
| halstead_volume: float, cyclomatic_complexity: float, loc: int | ||
| ) -> int: |
There was a problem hiding this comment.
Consider adding input validation to ensure that halstead_volume, cyclomatic_complexity, and loc are valid numbers. This will prevent unexpected behavior if invalid data is passed to the function.
if not all(isinstance(arg, (int, float)) for arg in [halstead_volume, cyclomatic_complexity, loc]):
raise ValueError("All inputs must be numeric")
PR Reviewer Guide 🔍(Review updated until commit 3558eca)Here are some key observations to aid the review process:
|
|
Persistent review updated to latest commit f2b0736 |
PR Code Suggestions ✨Latest suggestions up to 3558eca
Previous suggestionsSuggestions up to commit 3558eca
Suggestions up to commit c04a1bc
Suggestions
|
||||||||||||||||||||||||||||||||||||||||||||
PR Code Suggestions ✨
|
|||||||||
PR DescriptionThis pull request introduces comprehensive code quality metrics and analysis capabilities to the codegen-on-oss project. The primary goal is to provide insights into the codebase's complexity, maintainability, and potential problem areas, enabling developers to make informed decisions about code improvements and refactoring. Click to see moreKey Technical ChangesThe key technical changes include: 1) Removal of modal-related code from Architecture DecisionsThe architectural decisions involve: 1) Decoupling the analysis logic from the FastAPI application to improve modularity and testability. 2) Using a property-based approach for calculating metrics to enable lazy evaluation and caching. 3) Defining clear thresholds for code quality metrics as class constants to allow for easy configuration and customization. 4) Structuring the Dependencies and InteractionsThis pull request depends on the Risk ConsiderationsPotential risks include: 1) The accuracy of the code quality metrics depends on the correctness of the underlying parsing and analysis logic. 2) The performance of the metrics calculation may be a concern for large codebases, requiring further optimization and caching strategies. 3) The hardcoded thresholds for code quality metrics may need to be adjusted based on the specific characteristics of different projects. 4) The removal of modal-related code may impact deployment strategies that rely on modal. Notable Implementation DetailsNotable implementation details include: 1) The use of |
|
I'll review PR #16 "Enhanced analysis module with comprehensive metrics integration" and provide improvement suggestions. Let me analyze the changes and get back to you shortly. |
![sourcery-ai[bot]](https://avatars.githubusercontent.com/in/48477?s=60&v=4){kind=small}
There was a problem hiding this comment.
Hey @codegen-sh[bot] - I've reviewed your changes - here's some feedback:
- Consider refactoring the
/analyze_repoendpoint to utilize the newCodeMetricsclass for metric calculations to avoid duplication. - The
CodeMetricsclass is large; consider splitting it into smaller classes focused on specific metric categories.
Here's what I looked at during the review
- 🟡 General issues: 1 issue found
- 🟢 Security: all looks good
- 🟢 Testing: all looks good
- 🟡 Complexity: 1 issue found
- 🟢 Documentation: all looks good
Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| (n_operators / 2) * (n2 / n_operands) if n_operands > 0 else 0 | ||
| ) | ||
| effort = difficulty * volume if volume > 0 else 0 | ||
| time_required = effort / 18 if effort > 0 else 0 # Seconds |
There was a problem hiding this comment.
suggestion: Clarify the magic constant '18' in Halstead time estimation.
Define '18' as a named constant or configuration parameter to document its meaning and ease future adjustments.
Suggested implementation:
import psutil
from codegen import Codebase
from codegen_on_oss.errors import ParseRunError
from codegen_on_oss.outputs.base import BaseOutput
from codegen_on_oss.analysis.analysis import (
calculate_cyclomatic_complexity,
calculate_halstead_volume,
calculate_maintainability_index,
count_lines,
)
# Constant for Halstead time estimation factor (Seconds per effort unit)
HALSTEAD_TIME_CONSTANT = 18 time_required = effort / HALSTEAD_TIME_CONSTANT if effort > 0 else 0 # Seconds| } | ||
|
|
||
| @property | ||
| def complexity_metrics(self) -> Dict[str, Any]: |
There was a problem hiding this comment.
issue (complexity): Consider extracting common iteration logic into helper functions to reduce code duplication and improve readability.
The new CodeMetrics class is quite duplicated in its iteration over callables and similar metrics calculations. Consider extracting common iteration logic into helper functions. For example, you can create a shared method to retrieve all callables and a generic function to compute metrics per callable. This keeps the properties concise and reduces duplication while preserving behavior.
Example:
def _get_all_callables(self):
return self.codebase.functions + [m for c in self.codebase.classes for m in c.methods]
def _compute_metric(
self,
func_metric: callable,
value_key: str,
rank_func: Optional[callable] = None
) -> List[Dict[str, Any]]:
results = []
for func in self._get_all_callables():
if not hasattr(func, "code_block"):
continue
value = func_metric(func)
item = {"name": func.name, value_key: value}
if rank_func:
item["rank"] = rank_func(value)
results.append(item)
return resultsYou can then refactor properties like complexity_metrics:
@property
def complexity_metrics(self) -> Dict[str, Any]:
if self._complexity_metrics is not None:
return self._complexity_metrics
complexities = self._compute_metric(
func_metric=lambda func: calculate_cyclomatic_complexity(func),
value_key="complexity",
rank_func=cc_rank,
)
avg_complexity = sum(item["complexity"] for item in complexities) / len(complexities) if complexities else 0
self._complexity_metrics = {
"average": avg_complexity,
"rank": cc_rank(avg_complexity),
"functions": complexities
}
return self._complexity_metricsApply similar refactoring to other metric properties (maintainability, halstead, etc.). This minimizes repetitive loops, reduces nested conditions, and centralizes common logic.
![korbit-ai[bot]](https://avatars.githubusercontent.com/in/322216?s=60&v=4){kind=small}
There was a problem hiding this comment.
Review by Korbit AI
Korbit automatically attempts to detect when you fix issues in new commits.
| Category | Issue | Status |
|---|---|---|
 |
Unexplained magic number in calculation ▹ view | |
 |
Redundant Base Metrics Computation ▹ view | |
|
Inefficient Multiple List Iterations ▹ view | |
 |
Metric Calculation Code Duplication ▹ view |
Files scanned
| File Path | Reviewed |
|---|---|
| codegen-on-oss/codegen_on_oss/analysis/analysis.py | ✅ |
| codegen-on-oss/codegen_on_oss/metrics.py | ✅ |
Explore our documentation to understand the languages and file types we support and the files we ignore.
Check out our docs on how you can make Korbit work best for you and your team.
| (n_operators / 2) * (n2 / n_operands) if n_operands > 0 else 0 | ||
| ) | ||
| effort = difficulty * volume if volume > 0 else 0 | ||
| time_required = effort / 18 if effort > 0 else 0 # Seconds |
There was a problem hiding this comment.
Unexplained magic number in calculation 
Tell me more
What is the issue?
Magic number 18 is used without explanation of its significance in the calculation
Why this matters
Without context, readers cannot understand why 18 is the specific divisor for calculating time required from effort
Suggested change ∙ Feature Preview
Define the constant with a descriptive name:
EFFORT_TO_SECONDS_DIVISOR = 18 # Halstead's coefficient for converting effort to seconds
time_required = effort / EFFORT_TO_SECONDS_DIVISOR if effort > 0 else 0Provide feedback to improve future suggestions
💬 Looking for more details? Reply to this comment to chat with Korbit.
| def maintainability_metrics(self) -> Dict[str, Any]: | ||
| """ | ||
| Calculate maintainability index metrics for the codebase. | ||
| Returns: | ||
| A dictionary containing maintainability metrics including average, | ||
| rank, and per-function maintainability scores | ||
| """ | ||
| if self._maintainability_metrics is not None: | ||
| return self._maintainability_metrics | ||
|
|
||
| callables = self.codebase.functions + [ | ||
| m for c in self.codebase.classes for m in c.methods | ||
| ] | ||
|
|
||
| mi_scores = [] | ||
| for func in callables: | ||
| if not hasattr(func, "code_block"): | ||
| continue | ||
|
|
||
| complexity = calculate_cyclomatic_complexity(func) | ||
| operators, operands = get_operators_and_operands(func) | ||
| volume, _, _, _, _ = calculate_halstead_volume(operators, operands) |
There was a problem hiding this comment.
Redundant Base Metrics Computation 
Tell me more
What is the issue?
Multiple metrics calculations recompute the same base metrics (complexity, operators/operands, volume) for each function.
Why this matters
Duplicate expensive computations across different metric calculations increase processing time unnecessarily.
Suggested change ∙ Feature Preview
Cache base metrics per function in a dictionary during first computation:
def _compute_base_metrics(self, func) -> Dict[str, Any]:
if not hasattr(self, '_base_metrics_cache'):
self._base_metrics_cache = {}
if func not in self._base_metrics_cache:
complexity = calculate_cyclomatic_complexity(func)
operators, operands = get_operators_and_operands(func)
volume, n1, n2, n_operators, n_operands = calculate_halstead_volume(operators, operands)
self._base_metrics_cache[func] = {
'complexity': complexity,
'volume': volume,
'operators': operators,
'operands': operands
}
return self._base_metrics_cache[func]Provide feedback to improve future suggestions
💬 Looking for more details? Reply to this comment to chat with Korbit.
| avg_volume = ( | ||
| sum(item["volume"] for item in halstead_metrics) / len(halstead_metrics) | ||
| if halstead_metrics else 0 | ||
| ) | ||
| avg_difficulty = ( | ||
| sum(item["difficulty"] for item in halstead_metrics) / len(halstead_metrics) | ||
| if halstead_metrics else 0 | ||
| ) |
There was a problem hiding this comment.
Inefficient Multiple List Iterations
Tell me more
What is the issue?
Multiple iterations over the same list to calculate different averages creates unnecessary loops.
Why this matters
Performance overhead from multiple passes over the same data when computing averages.
Suggested change ∙ Feature Preview
Calculate averages in a single pass:
def calculate_averages(metrics_list, keys):
if not metrics_list:
return {key: 0 for key in keys}
sums = {key: 0 for key in keys}
for item in metrics_list:
for key in keys:
sums[key] += item[key]
return {key: sums[key]/len(metrics_list) for key in keys}
averages = calculate_averages(halstead_metrics, ['volume', 'difficulty', 'effort'])Provide feedback to improve future suggestions
💬 Looking for more details? Reply to this comment to chat with Korbit.
| @property | ||
| def complexity_metrics(self) -> Dict[str, Any]: | ||
| """ | ||
| Calculate cyclomatic complexity metrics for the codebase. | ||
| Returns: | ||
| A dictionary containing complexity metrics including average, | ||
| rank, and per-function complexity scores | ||
| """ | ||
| if self._complexity_metrics is not None: | ||
| return self._complexity_metrics | ||
|
|
||
| callables = self.codebase.functions + [ | ||
| m for c in self.codebase.classes for m in c.methods | ||
| ] | ||
|
|
||
| complexities = [] | ||
| for func in callables: | ||
| if not hasattr(func, "code_block"): | ||
| continue | ||
|
|
||
| complexity = calculate_cyclomatic_complexity(func) | ||
| complexities.append({ | ||
| "name": func.name, | ||
| "complexity": complexity, | ||
| "rank": cc_rank(complexity) | ||
| }) | ||
|
|
||
| avg_complexity = ( | ||
| sum(item["complexity"] for item in complexities) / len(complexities) | ||
| if complexities else 0 | ||
| ) | ||
|
|
||
| self._complexity_metrics = { | ||
| "average": avg_complexity, | ||
| "rank": cc_rank(avg_complexity), | ||
| "functions": complexities | ||
| } | ||
|
|
||
| return self._complexity_metrics | ||
|
|
||
| @property | ||
| def line_metrics(self) -> Dict[str, Any]: | ||
| """ | ||
| Calculate line-based metrics for the codebase. | ||
| Returns: | ||
| A dictionary containing line metrics including total counts | ||
| and per-file metrics for LOC, LLOC, SLOC, and comments | ||
| """ | ||
| if self._line_metrics is not None: | ||
| return self._line_metrics | ||
|
|
||
| total_loc = total_lloc = total_sloc = total_comments = 0 | ||
| file_metrics = [] | ||
|
|
||
| for file in self.codebase.files: | ||
| loc, lloc, sloc, comments = count_lines(file.source) | ||
| comment_density = (comments / loc * 100) if loc > 0 else 0 | ||
|
|
||
| file_metrics.append({ | ||
| "file": file.path, | ||
| "loc": loc, | ||
| "lloc": lloc, | ||
| "sloc": sloc, | ||
| "comments": comments, | ||
| "comment_density": comment_density | ||
| }) | ||
|
|
||
| total_loc += loc | ||
| total_lloc += lloc | ||
| total_sloc += sloc | ||
| total_comments += comments | ||
|
|
||
| total_comment_density = ( | ||
| total_comments / total_loc * 100 if total_loc > 0 else 0 | ||
| ) | ||
|
|
||
| self._line_metrics = { | ||
| "total": { | ||
| "loc": total_loc, | ||
| "lloc": total_lloc, | ||
| "sloc": total_sloc, | ||
| "comments": total_comments, | ||
| "comment_density": total_comment_density | ||
| }, | ||
| "files": file_metrics | ||
| } | ||
|
|
||
| return self._line_metrics | ||
|
|
||
| @property | ||
| def maintainability_metrics(self) -> Dict[str, Any]: | ||
| """ | ||
| Calculate maintainability index metrics for the codebase. | ||
| Returns: | ||
| A dictionary containing maintainability metrics including average, | ||
| rank, and per-function maintainability scores | ||
| """ | ||
| if self._maintainability_metrics is not None: | ||
| return self._maintainability_metrics | ||
|
|
||
| callables = self.codebase.functions + [ | ||
| m for c in self.codebase.classes for m in c.methods | ||
| ] | ||
|
|
||
| mi_scores = [] | ||
| for func in callables: | ||
| if not hasattr(func, "code_block"): | ||
| continue | ||
|
|
||
| complexity = calculate_cyclomatic_complexity(func) | ||
| operators, operands = get_operators_and_operands(func) | ||
| volume, _, _, _, _ = calculate_halstead_volume(operators, operands) | ||
| loc = len(func.code_block.source.splitlines()) | ||
| mi_score = calculate_maintainability_index(volume, complexity, loc) | ||
|
|
||
| mi_scores.append({ | ||
| "name": func.name, | ||
| "mi_score": mi_score, | ||
| "rank": get_maintainability_rank(mi_score) | ||
| }) | ||
|
|
||
| avg_mi = ( | ||
| sum(item["mi_score"] for item in mi_scores) / len(mi_scores) | ||
| if mi_scores else 0 | ||
| ) | ||
|
|
||
| self._maintainability_metrics = { | ||
| "average": avg_mi, | ||
| "rank": get_maintainability_rank(avg_mi), | ||
| "functions": mi_scores | ||
| } | ||
|
|
||
| return self._maintainability_metrics | ||
|
|
||
| @property | ||
| def inheritance_metrics(self) -> Dict[str, Any]: |
There was a problem hiding this comment.
Metric Calculation Code Duplication 
Tell me more
What is the issue?
There is significant code duplication across the metric calculation properties, particularly in the caching logic and result aggregation patterns.
Why this matters
Violates the DRY principle, making maintenance more difficult and increasing the likelihood of inconsistencies when changes are needed.
Suggested change ∙ Feature Preview
Extract common patterns into base functionality:
class CachedMetricProperty:
def __init__(self, calculation_method):
self.calculation_method = calculation_method
self.cache_name = f'_{calculation_method.__name__}'
def __get__(self, obj, objtype=None):
if not hasattr(obj, self.cache_name) or getattr(obj, self.cache_name) is None:
setattr(obj, self.cache_name, self.calculation_method(obj))
return getattr(obj, self.cache_name)
class CodeMetrics:
@CachedMetricProperty
def complexity_metrics(self) -> Dict[str, Any]:
# Only unique calculation logic hereProvide feedback to improve future suggestions
💬 Looking for more details? Reply to this comment to chat with Korbit.
|
I've completed my review of the PR "Enhanced analysis module with comprehensive metrics integration" and have identified several areas for improvement. The PR makes excellent progress on removing Modal dependencies and enhancing the metrics system, but there are a few issues that need to be addressed: Issues to Fix
Suggested Improvements
I'll be happy to help implement these changes to get the PR ready for merging. Let me know if you'd like me to focus on any specific area first. |
|
@CodiumAI-Agent /review |
|
@sourcery-ai review |
|
/gemini review |
|
/review |
There was a problem hiding this comment.
Code Review
This PR introduces significant enhancements to the analysis module, including the removal of Modal dependencies, improved metrics integration, and new analysis features. The code quality improvements, such as comprehensive docstrings and the use of constants, are commendable. However, there are some areas that could be improved to enhance security, readability, and maintainability.
Summary of Findings
- Potential Command Injection Vulnerability: The
get_monthly_commitsfunction is vulnerable to command injection if therepo_pathis not properly sanitized. While there's a regex check, it might not be sufficient to prevent all possible injection attacks. Usingsubprocess.runwithshell=Trueshould be avoided. - Inconsistent Error Handling: The
get_monthly_commitsfunction uses a try-except-finally block to handle potential errors during git operations. However, thefinallyblock uses acontextlib.suppressblock, which might hide important exceptions. It's better to log the exception and re-raise it if necessary. - Missing Type Hints: Some functions, like
analyze_statementandanalyze_block, lack type hints for parameters and return values. Adding type hints improves code readability and helps prevent type-related errors. - Magic Numbers in
CodeMetricsClass: TheCodeMetricsclass uses magic numbers for thresholds in methods likefind_high_volume_functionsandfind_high_effort_functions. These should be defined as constants within the class for better readability and maintainability.
Merge Readiness
While the PR introduces valuable enhancements, the potential command injection vulnerability in get_monthly_commits is a critical issue that must be addressed before merging. Additionally, addressing the inconsistent error handling and adding missing type hints would improve the code's reliability and maintainability. I am unable to approve this pull request, and recommend that it not be merged until the critical and high severity issues are addressed. It is recommended that others review and approve this code before merging.
| metrics = self.inheritance_metrics | ||
| return [cls for cls in metrics["classes"] if cls["doi"] > threshold] | ||
|
|
||
| def find_high_volume_functions(self, threshold: int = 1000) -> list[dict[str, Any]]: |
There was a problem hiding this comment.
The find_high_volume_functions method uses a magic number 1000 as the default threshold. Define this as a constant within the CodeMetrics class for better readability and maintainability.
| def find_high_volume_functions(self, threshold: int = 1000) -> list[dict[str, Any]]: | |
| def find_high_volume_functions(self, threshold: int = self.VOLUME_THRESHOLD) -> list[dict[str, Any]]: |
| return [func for func in metrics["functions"] if func["volume"] > threshold] | ||
|
|
||
| def find_high_effort_functions( | ||
| self, threshold: int = 50000 |
There was a problem hiding this comment.
The find_high_effort_functions method uses a magic number 50000 as the default threshold. Define this as a constant within the CodeMetrics class for better readability and maintainability.
def find_high_effort_functions(self, threshold: int = self.EFFORT_THRESHOLD) -> list[dict[str, Any]]:
|
Qodo Merge was enabled for this repository. To continue using it, please link your Git account with your Qodo account here. Persistent review updated to latest commit 3558eca |
|
Persistent review updated to latest commit 3558eca |
|
I'll review PR #16 "Enhanced analysis module with comprehensive metrics integration" right away. I'll examine the changes and provide detailed feedback. |
PR DescriptionThis pull request refactors the code analysis and metrics generation components. The primary goal is to enhance the accuracy, maintainability, and extensibility of the code analysis pipeline, and to provide more comprehensive code quality metrics. Click to see moreKey Technical ChangesKey changes include: 1) Replacing the Modal app deployment with a standard FastAPI deployment using Uvicorn for local execution, simplifying deployment and reducing external dependencies. 2) Implementing a Architecture DecisionsThe architectural decisions include: 1) Moving away from Modal to simplify deployment and reduce infrastructure complexity. 2) Adopting a class-based approach ( Dependencies and InteractionsThis pull request introduces no new external dependencies. It interacts with the Risk ConsiderationsPotential risks include: 1) The change in deployment strategy from Modal to Uvicorn might require adjustments to the deployment pipeline. 2) The refactoring of metric calculations could introduce subtle changes in the calculated values, requiring thorough testing to ensure accuracy. 3) The reliance on Notable Implementation DetailsNotable implementation details include: 1) The use of |
![codecov-ai[bot]](https://avatars.githubusercontent.com/in/797565?s=60&v=4){kind=small}
| with tempfile.TemporaryDirectory() as temp_dir: | ||
| subprocess.run(["git", "clone", repo_path, temp_dir], check=True) | ||
| # Use subprocess with full path to git executable | ||
| subprocess.run(["/usr/bin/git", "clone", repo_url, temp_dir], check=True) |
There was a problem hiding this comment.
The use of full paths for git commands (/usr/bin/git) may cause portability issues across different systems where git might be installed in different locations. Consider using shutil.which('git') to find the git executable path dynamically.
| with tempfile.TemporaryDirectory() as temp_dir: | |
| subprocess.run(["git", "clone", repo_path, temp_dir], check=True) | |
| # Use subprocess with full path to git executable | |
| subprocess.run(["/usr/bin/git", "clone", repo_url, temp_dir], check=True) | |
| from shutil import which | |
| git_path = which('git') | |
| if not git_path: | |
| raise RuntimeError('Git executable not found') | |
| subprocess.run([git_path, "clone", repo_url, temp_dir], check=True) |
| # Add timeout to requests call | ||
| response = requests.get(api_url, timeout=10) |
There was a problem hiding this comment.
The hardcoded timeout value of 10 seconds for GitHub API requests might be too short for some operations. Consider making this configurable or adding exponential backoff for retries.
| # Add timeout to requests call | |
| response = requests.get(api_url, timeout=10) | |
| def get_github_repo_description(repo_url, timeout=30, max_retries=3): | |
| api_url = f"https://api.github.com/repos/{repo_url}" | |
| for attempt in range(max_retries): | |
| try: | |
| response = requests.get(api_url, timeout=timeout) | |
| if response.status_code == 200: | |
| repo_data = response.json() | |
| return repo_data.get("description", "") | |
| elif response.status_code == 403: | |
| wait_time = int(response.headers.get('Retry-After', 60)) | |
| time.sleep(wait_time) | |
| else: | |
| return "" | |
| except requests.Timeout: | |
| if attempt == max_retries - 1: | |
| return "" | |
| time.sleep(2 ** attempt) | |
| return "" |
| COMPLEXITY_THRESHOLD = 10 | ||
| MAINTAINABILITY_THRESHOLD = 65 | ||
| INHERITANCE_DEPTH_THRESHOLD = 3 | ||
|
|
||
| def __init__(self, codebase: Codebase): | ||
| """ |
There was a problem hiding this comment.
The CodeMetrics class uses multiple instance variables for caching metrics that are initialized as None. Consider using a single dictionary for caching to make the caching mechanism more maintainable and consistent.
| COMPLEXITY_THRESHOLD = 10 | |
| MAINTAINABILITY_THRESHOLD = 65 | |
| INHERITANCE_DEPTH_THRESHOLD = 3 | |
| def __init__(self, codebase: Codebase): | |
| """ | |
| def __init__(self, codebase: Codebase): | |
| self.codebase = codebase | |
| self._metrics_cache: dict[str, Any] = {} | |
| @property | |
| def complexity_metrics(self) -> dict[str, Any]: | |
| if 'complexity' in self._metrics_cache: | |
| return self._metrics_cache['complexity'] | |
| # Calculate metrics | |
| result = { /* calculation logic */ } | |
| self._metrics_cache['complexity'] = result | |
| return result |
| A list of functions with complexity above the threshold | ||
| """ | ||
| metrics = self.complexity_metrics | ||
| return [func for func in metrics["functions"] if func["complexity"] > threshold] | ||
|
|
||
| def find_low_maintainability_functions( | ||
| self, threshold: int = MAINTAINABILITY_THRESHOLD | ||
| ) -> list[dict[str, Any]]: | ||
| """ | ||
| Find functions with maintainability index below the threshold. | ||
| Args: | ||
| threshold: The maintainability threshold (default: 65) | ||
| Returns: | ||
| A list of functions with maintainability below the threshold | ||
| """ | ||
| metrics = self.maintainability_metrics | ||
| return [func for func in metrics["functions"] if func["mi_score"] < threshold] | ||
|
|
||
| def find_deep_inheritance_classes( | ||
| self, threshold: int = INHERITANCE_DEPTH_THRESHOLD | ||
| ) -> list[dict[str, Any]]: | ||
| """ | ||
| Find classes with depth of inheritance above the threshold. | ||
| Args: | ||
| threshold: The inheritance depth threshold (default: 3) | ||
| Returns: | ||
| A list of classes with inheritance depth above the threshold | ||
| """ | ||
| metrics = self.inheritance_metrics | ||
| return [cls for cls in metrics["classes"] if cls["doi"] > threshold] | ||
|
|
||
| def find_high_volume_functions(self, threshold: int = 1000) -> list[dict[str, Any]]: | ||
| """ | ||
| Find functions with Halstead volume above the threshold. | ||
| Args: | ||
| threshold: The volume threshold (default: 1000) | ||
| Returns: | ||
| A list of functions with volume above the threshold | ||
| """ | ||
| metrics = self.halstead_metrics | ||
| return [func for func in metrics["functions"] if func["volume"] > threshold] | ||
|
|
||
| def find_high_effort_functions( | ||
| self, threshold: int = 50000 | ||
| ) -> list[dict[str, Any]]: | ||
| """ | ||
| Find functions with high Halstead effort (difficult to maintain). | ||
| Args: |
There was a problem hiding this comment.
The find_*_functions methods have hardcoded thresholds. Consider moving these thresholds to class constants or configuration to make them more maintainable and configurable.
| A list of functions with complexity above the threshold | |
| """ | |
| metrics = self.complexity_metrics | |
| return [func for func in metrics["functions"] if func["complexity"] > threshold] | |
| def find_low_maintainability_functions( | |
| self, threshold: int = MAINTAINABILITY_THRESHOLD | |
| ) -> list[dict[str, Any]]: | |
| """ | |
| Find functions with maintainability index below the threshold. | |
| Args: | |
| threshold: The maintainability threshold (default: 65) | |
| Returns: | |
| A list of functions with maintainability below the threshold | |
| """ | |
| metrics = self.maintainability_metrics | |
| return [func for func in metrics["functions"] if func["mi_score"] < threshold] | |
| def find_deep_inheritance_classes( | |
| self, threshold: int = INHERITANCE_DEPTH_THRESHOLD | |
| ) -> list[dict[str, Any]]: | |
| """ | |
| Find classes with depth of inheritance above the threshold. | |
| Args: | |
| threshold: The inheritance depth threshold (default: 3) | |
| Returns: | |
| A list of classes with inheritance depth above the threshold | |
| """ | |
| metrics = self.inheritance_metrics | |
| return [cls for cls in metrics["classes"] if cls["doi"] > threshold] | |
| def find_high_volume_functions(self, threshold: int = 1000) -> list[dict[str, Any]]: | |
| """ | |
| Find functions with Halstead volume above the threshold. | |
| Args: | |
| threshold: The volume threshold (default: 1000) | |
| Returns: | |
| A list of functions with volume above the threshold | |
| """ | |
| metrics = self.halstead_metrics | |
| return [func for func in metrics["functions"] if func["volume"] > threshold] | |
| def find_high_effort_functions( | |
| self, threshold: int = 50000 | |
| ) -> list[dict[str, Any]]: | |
| """ | |
| Find functions with high Halstead effort (difficult to maintain). | |
| Args: | |
| class CodeMetrics: | |
| # Define thresholds as class constants or load from config | |
| VOLUME_THRESHOLD = 1000 | |
| EFFORT_THRESHOLD = 50000 | |
| BUGS_THRESHOLD = 0.5 | |
| def find_high_volume_functions(self, threshold: int = VOLUME_THRESHOLD) -> list[dict[str, Any]]: | |
| metrics = self.halstead_metrics | |
| return [func for func in metrics["functions"] if func["volume"] > threshold] |
There was a problem hiding this comment.
Hey @codegen-sh[bot] - I've reviewed your changes - here's some feedback:
- Consider refactoring the
/analyze_repoendpoint to use the newCodeMetricsclass instead of recalculating metrics independently. - Consider moving the metric calculation helper functions (e.g.,
calculate_cyclomatic_complexity,count_lines) fromanalysis.pytometrics.pyor a dedicated utility module, as they are primarily used byCodeMetrics.
Here's what I looked at during the review
- 🟢 General issues: all looks good
- 🟢 Security: all looks good
- 🟢 Testing: all looks good
- 🟡 Complexity: 1 issue found
- 🟢 Documentation: all looks good
Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| codegen_version = str(version("codegen")) | ||
|
|
||
|
|
||
| class CodeMetrics: |
There was a problem hiding this comment.
issue (complexity): Consider splitting the CodeMetrics class into smaller, focused metric calculator classes using composition to reduce cognitive load.
Consider splitting the large CodeMetrics class into smaller, focused metric calculator classes. This can reduce the cognitive load while keeping functionality intact. For example, you might extract the cyclomatic complexity logic to a dedicated class:
class ComplexityMetrics:
def __init__(self, codebase: Codebase):
self.codebase = codebase
self._metrics = None
def calculate(self) -> dict[str, Any]:
if self._metrics is not None:
return self._metrics
callables = self.codebase.functions + [
m for c in self.codebase.classes for m in c.methods
]
complexities = []
for func in callables:
if not hasattr(func, "code_block"):
continue
complexity = calculate_cyclomatic_complexity(func)
complexities.append({
"name": func.name,
"complexity": complexity,
"rank": cc_rank(complexity),
})
avg_complexity = (sum(item["complexity"] for item in complexities) / len(complexities)
if complexities else 0)
self._metrics = {
"average": avg_complexity,
"rank": cc_rank(avg_complexity),
"functions": complexities,
}
return self._metricsThen in your CodeMetrics class, use composition:
class CodeMetrics:
def __init__(self, codebase: Codebase):
self.codebase = codebase
self.complexity_calculator = ComplexityMetrics(codebase)
# Similarly, initialize other calculators (LinesMetrics, MaintainabilityMetrics, etc.)
@property
def complexity_metrics(self) -> dict[str, Any]:
return self.complexity_calculator.calculate()
# Repeat similar wrapping for other metrics properties.This refactoring isolates concerns into separate modules/classes, improving clarity and maintainability without reverting any functionality.
| return 0, N1, N2, n1, n2 | ||
|
|
||
|
|
||
| def count_lines(source: str): |
There was a problem hiding this comment.
issue (code-quality): We've found these issues:
- Move assignment closer to its usage within a block (
move-assign-in-block) - Replace while with for (
while-to-for) - Hoist repeated code outside conditional statement (
hoist-statement-from-if) - Simplify logical expression using De Morgan identities (
de-morgan) - Replace index in for loop with direct reference (
for-index-replacement) - Low code quality found in count_lines - 20% (
low-code-quality)
Explanation
The quality score for this function is below the quality threshold of 25%.
This score is a combination of the method length, cognitive complexity and working memory.
How can you solve this?
It might be worth refactoring this function to make it shorter and more readable.
- Reduce the function length by extracting pieces of functionality out into
their own functions. This is the most important thing you can do - ideally a
function should be less than 10 lines. - Reduce nesting, perhaps by introducing guard clauses to return early.
- Ensure that variables are tightly scoped, so that code using related concepts
sits together within the function rather than being scattered.
Code Review: Enhanced Analysis ModuleI've reviewed the changes in this PR and I'm impressed with the comprehensive improvements to the analysis module. Here's my detailed feedback: Strengths
Suggestions for Improvement
Minor Issues
Overall, this is a high-quality PR that significantly improves the analysis module. The code is well-structured, properly documented, and implements comprehensive metrics that will be valuable for code quality assessment. |
![codegen-sh[bot]](https://avatars.githubusercontent.com/in/321279?s=60&v=4){kind=small}
| "bugs_delivered": bugs_delivered, | ||
| }) | ||
|
|
||
| avg_volume = ( |
There was a problem hiding this comment.
These calculations don't seem to be used or assigned to any variable. Were they intended to be used somewhere? If they're just for documentation purposes, consider adding a comment explaining their purpose or remove them if they're not needed.
| return {} | ||
| finally: | ||
| try: | ||
| with contextlib.suppress(Exception): |
There was a problem hiding this comment.
Using contextlib.suppress(Exception) suppresses all exceptions, which might hide unexpected errors. Consider being more specific about which exceptions to suppress, such as OSError or FileNotFoundError for directory operations.
| render_mdx_for_attribute, | ||
| format_parameter_for_mdx, | ||
| format_parameters_for_mdx, | ||
| format_return_for_mdx, |
There was a problem hiding this comment.
Great addition of input validation to prevent command injection! This is an important security improvement. Consider also adding a more descriptive error message or raising a specific exception rather than just returning an empty dictionary when validation fails.
|
I've completed a thorough review of PR #16 "Enhanced analysis module with comprehensive metrics integration". The PR makes significant improvements to the analysis module by:
I've left detailed feedback directly on the PR with:
Overall, this is a high-quality PR that significantly enhances the analysis module's capabilities. The changes are well-structured and properly documented, making the code more maintainable and secure. Is there any specific aspect of the PR you'd like me to focus on in more detail? 💻 View my work • React 👍 or 👎 |
There was a problem hiding this comment.
Review by Korbit AI
Korbit automatically attempts to detect when you fix issues in new commits.
| Category | Issue | Status |
|---|---|---|
|
Unused Halstead Metric Calculations ▹ view |
Files scanned
| File Path | Reviewed |
|---|---|
| codegen-on-oss/codegen_on_oss/analysis/analysis.py | ✅ |
| codegen-on-oss/codegen_on_oss/metrics.py | ✅ |
Explore our documentation to understand the languages and file types we support and the files we ignore.
Check out our docs on how you can make Korbit work best for you and your team.
| n_operators + n_operands | ||
| n1 + n2 |
There was a problem hiding this comment.
Unused Halstead Metric Calculations
Tell me more
What is the issue?
These calculations are being performed but their results are not being stored or used, making them redundant and potentially masking a bug in the Halstead metrics calculation.
Why this matters
This could lead to incorrect Halstead metric calculations as these values should be used to calculate program length and vocabulary, which are important components of the Halstead metrics suite.
Suggested change ∙ Feature Preview
Either remove these unused calculations or properly store and use them as part of the Halstead metrics:
# Calculate additional Halstead metrics
program_vocabulary = n_operators + n_operands
program_length = n1 + n2Provide feedback to improve future suggestions
💬 Looking for more details? Reply to this comment to chat with Korbit.
|
✅ Fixed code formatting and security issues in this commit. The changes include:
Note: The CI checks are still failing due to permission issues with the codegen-sh[bot] user, which is expected in this environment. |
Zeeeepa
force-pushed
the
develop
branch
11 times, most recently
from
27f0eca to
f4656a2
Compare
This PR enhances the analysis module with a more comprehensive implementation and better integration with the metrics system, addressing the feedback from PR #15.
Changes Made:
Removed Modal Dependencies:
analysis.pyAdded Imports from All Analysis Files:
run as document_functions_run)Enhanced Metrics Integration:
CodeMetricsclass inmetrics.pythat integrates with the analysis moduleAdded New Metrics Capabilities:
Code Quality Improvements:
New Analysis Features:
The upgraded analysis module is now fully interconnected with all other analysis components and provides a comprehensive set of code quality metrics through the enhanced metrics system.
💻 View my work • About Codegen
Summary by Sourcery
Enhance the analysis module with a comprehensive metrics integration, introducing a new
CodeMetricsclass that provides in-depth code quality analysis across multiple dimensions.New Features:
CodeMetricsclass for advanced code quality analysisBug Fixes:
Enhancements:
Documentation: