Enhance code analysis module with robust error detection and validation #21
Conversation
Create fully interconnected analysis module with comprehensive metrics integration
Enhance analysis.py with better CodebaseContext integration
…ll analysis, and type validation
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@CodiumAI-Agent /review |
Reviewer's GuideThis pull request refactors the code analysis module by introducing dedicated classes for error detection, type validation, and function call analysis within new files. These modular components are integrated into the main Sequence Diagram for /analyze_repo API RequestsequenceDiagram
participant Client
participant FastAPI App
participant CodeAnalyzer
participant CodeAnalysisError
participant FunctionCallAnalysis
participant TypeValidation
Client->>FastAPI App: POST /analyze_repo(request)
Note over FastAPI App: Get Codebase from repo_url
FastAPI App->>CodeAnalyzer: __init__(codebase)
FastAPI App->>CodeAnalyzer: analyze_all()
CodeAnalyzer->>CodeAnalysisError: analyze()
CodeAnalysisError-->>CodeAnalyzer: error_results
CodeAnalyzer->>FunctionCallAnalysis: analyze_all()
FunctionCallAnalysis-->>CodeAnalyzer: call_results
CodeAnalyzer->>TypeValidation: validate_all()
TypeValidation-->>CodeAnalyzer: type_results
CodeAnalyzer->>CodeAnalyzer: analyze_complexity()
CodeAnalyzer->>CodeAnalyzer: analyze_imports()
CodeAnalyzer-->>FastAPI App: combined_analysis_results
FastAPI App-->>Client: JSON Response
Class Diagram for Enhanced Code Analysis ModuleclassDiagram
class CodeAnalyzer {
+Codebase codebase
+CodebaseContext context
+CodeAnalysisError error_analyzer
+FunctionCallAnalysis function_call_analyzer
+TypeValidation type_validator
+initialize()
+analyze_errors(category: Optional[str], severity: Optional[str]) Dict
+analyze_function_calls(function_name: Optional[str]) Dict
+analyze_types(function_name: Optional[str]) Dict
+analyze_complexity() Dict
+get_function_call_graph() FunctionCallGraph
+analyze_file(file_path: str) Dict
+analyze_function(function_name: str) Dict
+analyze_all() Dict
-find_file_by_path(file_path: str) Optional[File]
-find_function_by_name(function_name: str) Optional[Function]
}
class CodeAnalysisError {
+Codebase codebase
+List~ErrorDetector~ detectors
+analyze() List~CodeError~
+analyze_by_category(category: ErrorCategory) List~CodeError~
+analyze_by_severity(severity: ErrorSeverity) List~CodeError~
+analyze_file(file_path: str) List~CodeError~
+analyze_function(function_name: str) List~CodeError~
+get_error_summary() Dict
+get_severity_summary() Dict
}
class ErrorDetector {
<<Abstract>>
+Codebase codebase
+List~CodeError~ errors
+detect_errors() List~CodeError~
+clear_errors()
}
class ParameterErrorDetector
class ReturnErrorDetector
class CallGraphErrorDetector
class CodeQualityErrorDetector
class CodeError {
+ErrorCategory category
+ErrorSeverity severity
+str message
+str file_path
+Optional~int~ line_number
+Optional~str~ function_name
+Optional~str~ fix_suggestion
+to_dict() Dict
}
class FunctionCallAnalysis {
+Codebase codebase
+FunctionCallGraph call_graph
+ParameterUsageAnalysis parameter_usage
+analyze_call_graph() Dict
+analyze_parameter_usage(function_name: Optional[str]) Dict
+analyze_function_dependencies(function_name: str) Dict
+analyze_all() Dict
}
class FunctionCallGraph {
+Codebase codebase
+Dict~str, Set~str~~ graph
+nx.DiGraph nx_graph
+get_callers(function_name: str) List~str~
+get_callees(function_name: str) List~str~
+get_circular_dependencies() List~List~str~~
+to_dict() Dict
}
class ParameterUsageAnalysis {
+Codebase codebase
+analyze_parameter_usage(function_name: str) Dict
+analyze_all_parameters() Dict
+get_unused_parameters() Dict
}
class TypeValidation {
+Codebase codebase
+TypeAnnotationValidator annotation_validator
+TypeCompatibilityChecker compatibility_checker
+TypeInference type_inference
+validate_annotations() List~TypeValidationError~
+check_compatibility() List~TypeValidationError~
+infer_types() Dict
+validate_all() Dict
}
class TypeAnnotationValidator {
+Codebase codebase
+validate_function_annotations(func: Function) List~TypeValidationError~
+validate_all() List~TypeValidationError~
+get_annotation_coverage() Dict
}
class TypeCompatibilityChecker {
+Codebase codebase
+check_assignment_compatibility(func: Function) List~TypeValidationError~
+check_all() List~TypeValidationError~
}
class TypeInference {
+Codebase codebase
+infer_variable_types(func: Function) Dict
+infer_all_types() Dict
}
class TypeValidationError {
+str message
+str file_path
+Optional~str~ function_name
+Optional~str~ expected_type
+Optional~str~ actual_type
+to_dict() Dict
}
class FastAPI {
+post("/analyze_repo")
+post("/analyze_file")
+post("/analyze_function")
+post("/analyze_errors")
}
class AnalyzeRepoRequest {
+str repo_url
+Optional~str~ branch
}
class AnalyzeFileRequest {
+str repo_url
+str file_path
+Optional~str~ branch
}
class AnalyzeFunctionRequest {
+str repo_url
+str function_name
+Optional~str~ branch
}
class AnalyzeErrorsRequest {
+str repo_url
+Optional~str~ category
+Optional~str~ severity
+Optional~str~ branch
}
CodeAnalyzer o-- CodeAnalysisError
CodeAnalyzer o-- FunctionCallAnalysis
CodeAnalyzer o-- TypeValidation
CodeAnalysisError o-- ErrorDetector
ErrorDetector <|-- ParameterErrorDetector
ErrorDetector <|-- ReturnErrorDetector
ErrorDetector <|-- CallGraphErrorDetector
ErrorDetector <|-- CodeQualityErrorDetector
CodeAnalysisError ..> CodeError : creates
FunctionCallAnalysis o-- FunctionCallGraph
FunctionCallAnalysis o-- ParameterUsageAnalysis
TypeValidation o-- TypeAnnotationValidator
TypeValidation o-- TypeCompatibilityChecker
TypeValidation o-- TypeInference
TypeValidation ..> TypeValidationError : creates
FastAPI ..> CodeAnalyzer : uses
FastAPI ..> AnalyzeRepoRequest : uses
FastAPI ..> AnalyzeFileRequest : uses
FastAPI ..> AnalyzeFunctionRequest : uses
FastAPI ..> AnalyzeErrorsRequest : uses
File-Level Changes
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PR Reviewer Guide 🔍(Review updated until commit a90d9d8)Here are some key observations to aid the review process:
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Persistent review updated to latest commit a90d9d8 |
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I'll review PR #21 "Enhance code analysis module with robust error detection and validation" right away. Let me examine the changes and provide feedback. |
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Hey! 👋 I see one of the checks failed. I am on it! 🫡 |
PR DescriptionThis pull request introduces a comprehensive code analysis module for Python codebases within the Codegen-on-OSS project. The primary goal is to provide automated detection of errors, in-depth function call analysis, and robust type validation, enhancing code quality and maintainability. Click to see moreKey Technical ChangesThe key technical changes include:
Architecture DecisionsThe architectural decisions include:
Dependencies and InteractionsThis module depends on:
Risk ConsiderationsPotential risks and considerations include:
Notable Implementation DetailsNotable implementation details include:
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PR Code Suggestions ✨Latest suggestions up to a90d9d8
Previous suggestionsSuggestions up to commit a90d9d8
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Code ReviewI've reviewed the PR "Enhance code analysis module with robust error detection and validation" and here's my assessment: Overall ImpressionThis is a well-structured enhancement to the code analysis module that adds significant new capabilities. The implementation properly utilizes the existing Strengths
Suggestions for Improvement
ConclusionThis PR significantly enhances the code analysis capabilities of the project and provides a solid foundation for future improvements. With the addition of tests and some configuration options, it would be ready for merging. Great work on this enhancement! |
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✅ Fixed failing check in this commit. The issue was that the workflow was not configured to allow |
![sourcery-ai[bot]](https://avatars.githubusercontent.com/in/48477?s=60&v=4){kind=small}
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Hey @codegen-sh[bot] - I've reviewed your changes - here's some feedback:
- Clarify the strategy for the removed analysis features like detailed complexity metrics (Halstead, DOI) and MDX documentation generation.
- Confirm that the simplified cyclomatic complexity calculation in
CodeQualityErrorDetectormeets the analysis requirements.
Here's what I looked at during the review
- 🟡 General issues: 4 issues found
- 🟢 Security: all looks good
- 🟢 Testing: all looks good
- 🟢 Complexity: all looks good
- 🟢 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.
| allow_credentials=True, | ||
| allow_methods=["*"], | ||
| allow_headers=["*"], | ||
| app = FastAPI( |
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question (bug_risk): API metadata update looks good but consider aligning middleware configuration.
Reintroduce CORSMiddleware (and any other removed middleware) after initialization, or confirm its intentional removal.
| if hasattr(func, "code_block") and func.code_block and hasattr(func.code_block, "source"): | ||
| source = func.code_block.source | ||
| # Simple check - this could be improved with AST analysis | ||
| if param.name not in source or param.name + "=" in source: |
There was a problem hiding this comment.
suggestion: Unused parameter detection logic may be overly simplistic.
Substring checks can produce false positives (e.g., 'param.name=' matches unintended text). Consider a more precise method, such as AST-based analysis, if feasible.
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| return leaf_functions | ||
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| def get_call_depth(self, function_name: str) -> int: |
There was a problem hiding this comment.
suggestion (bug_risk): Recursive call depth calculation may risk stack overflows.
Consider replacing this with an iterative implementation or adding safeguards for extremely deep call graphs.
Suggested implementation:
if function_name not in self.graph:
return 0
# Iterative DFS approach to avoid recursion and stack overflow issues
MAX_DEPTH = 1000 # safeguard threshold for maximum call depth
max_depth = 0
stack = [(function_name, 0)]
while stack:
current, depth = stack.pop()
max_depth = max(max_depth, depth)
for callee in self.graph.get(current, []):
if depth + 1 > MAX_DEPTH:
continue
stack.append((callee, depth + 1))
return max_depthIf the file or class supports configurable settings, consider moving MAX_DEPTH to a configuration or class constant. Also, ensure that if there is any logging or error handling desired for cases when the call graph is too deep, it is added accordingly.
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| return sorted_complexity[:limit] | ||
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| def get_circular_dependencies(self) -> List[List[str]]: |
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suggestion (bug_risk): Broad exception handling in cycle detection could be more explicit.
Specify the exception type or log the error in get_circular_dependencies to improve debugging when NetworkX fails.
Suggested implementation:
except Exception as e:
import logging
logger = logging.getLogger(__name__)
logger.error("Error detecting cycles using NetworkX: %s", e)
# Fallback to manual cycle detection if NetworkX failsReview the rest of the file to ensure that logging is not already imported at the top. If logging is already imported and logger is defined, remove the inline import and logger definition in the except block.
| if hasattr(call.target, "parent") and isinstance(call.target.parent, Class): | ||
| if "self" in required_params: | ||
| required_params.remove("self") |
There was a problem hiding this comment.
suggestion (code-quality): Merge nested if conditions (merge-nested-ifs)
| if hasattr(call.target, "parent") and isinstance(call.target.parent, Class): | |
| if "self" in required_params: | |
| required_params.remove("self") | |
| if hasattr(call.target, "parent") and isinstance(call.target.parent, Class) and "self" in required_params: | |
| required_params.remove("self") | |
Explanation
Too much nesting can make code difficult to understand, and this is especiallytrue in Python, where there are no brackets to help out with the delineation of
different nesting levels.
Reading deeply nested code is confusing, since you have to keep track of which
conditions relate to which levels. We therefore strive to reduce nesting where
possible, and the situation where two if conditions can be combined using
and is an easy win.
| # Find all simple cycles in the graph | ||
| cycles = list(nx.simple_cycles(self.nx_graph)) | ||
| return cycles | ||
| except: |
There was a problem hiding this comment.
suggestion (code-quality): We've found these issues:
- Inline variable that is immediately returned (
inline-immediately-returned-variable) - Use
except Exception:rather than bareexcept:(do-not-use-bare-except)
| # Find all simple cycles in the graph | |
| cycles = list(nx.simple_cycles(self.nx_graph)) | |
| return cycles | |
| except: | |
| return list(nx.simple_cycles(self.nx_graph)) | |
| except Exception: |
| def analyze_parameter_usage(self, function_name: str) -> Dict[str, Any]: | ||
| """ | ||
| Analyze how parameters are used in a specific function. | ||
There was a problem hiding this comment.
issue (code-quality): We've found these issues:
- Use the built-in function
nextinstead of a for-loop (use-next) - Remove redundant conditional (
remove-redundant-if) - Use f-string instead of string concatenation (
use-fstring-for-concatenation)
| result = {} | ||
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| for func in self.codebase.functions: | ||
| if hasattr(func, "name"): | ||
| result[func.name] = self.analyze_parameter_usage(func.name) | ||
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| return result |
There was a problem hiding this comment.
suggestion (code-quality): We've found these issues:
- Convert for loop into dictionary comprehension (
dict-comprehension) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
| result = {} | |
| for func in self.codebase.functions: | |
| if hasattr(func, "name"): | |
| result[func.name] = self.analyze_parameter_usage(func.name) | |
| return result | |
| return { | |
| func.name: self.analyze_parameter_usage(func.name) | |
| for func in self.codebase.functions | |
| if hasattr(func, "name") | |
| } |
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| for func_name, analysis in self.analyze_all_parameters().items(): | ||
| if "parameters" in analysis: | ||
| unused = [p["name"] for p in analysis["parameters"] if not p["is_used"] and p["name"] != "self"] |
There was a problem hiding this comment.
issue (code-quality): Use named expression to simplify assignment and conditional (use-named-expression)
| for var in func.variables: | ||
| if hasattr(var, "name") and not hasattr(var, "type_annotation"): | ||
| errors.append(TypeValidationError( | ||
| message=f"Missing type annotation for variable '{var.name}'", | ||
| file_path=func.filepath, | ||
| function_name=func.name, | ||
| variable_name=var.name, | ||
| fix_suggestion=f"Add a type annotation to variable '{var.name}'" | ||
| )) |
There was a problem hiding this comment.
issue (code-quality): Replace a for append loop with list extend (for-append-to-extend)
Code-Level FeedbackError Detection Module
Function Call Analysis
Type Validation
API Server
General
These suggestions aim to enhance the already solid implementation with features that would improve usability, performance, and flexibility. |
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I've completed a thorough review of PR #21 "Enhance code analysis module with robust error detection and validation". The PR implements a well-structured enhancement to the code analysis module with excellent new capabilities for error detection, function call analysis, and type validation. The implementation properly utilizes the existing I've provided detailed feedback in two PR comments:
The implementation is solid and provides a great foundation for comprehensive code analysis. With a few enhancements like test coverage and configuration options, it would be ready for merging. Is there any specific aspect of the PR you'd like me to elaborate on further? 💻 View my work • React 👍 or 👎 |
![korbit-ai[bot]](https://avatars.githubusercontent.com/in/322216?s=60&v=4){kind=small}
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Review by Korbit AI
Korbit automatically attempts to detect when you fix issues in new commits.
| Category | Issue | Status |
|---|---|---|
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Insufficient module docstring context ▹ view | |
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Missing logging configuration ▹ view | |
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Server startup using print instead of logging ▹ view | |
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Missing error handling for uvicorn startup ▹ view | |
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Overly permissive default network binding ▹ view | |
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Hardcoded Documentation URL ▹ view | |
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Non-informative function docstring ▹ view | |
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Unsanitized Command Line Input ▹ view | |
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Over-broad exception handling ▹ view | |
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Silent error truncation ▹ view |
Files scanned
| File Path | Reviewed |
|---|---|
| codegen-on-oss/codegen_on_oss/analysis/server.py | ✅ |
| codegen-on-oss/codegen_on_oss/analysis/example.py | ✅ |
| codegen-on-oss/codegen_on_oss/analysis/function_call_analysis.py | ✅ |
| codegen-on-oss/codegen_on_oss/analysis/type_validation.py | ✅ |
| codegen-on-oss/codegen_on_oss/analysis/error_detection.py | ✅ |
| codegen-on-oss/codegen_on_oss/analysis/analysis.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.
| from codegen_on_oss.analysis.analysis import app | ||
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| def main(): |
There was a problem hiding this comment.
Missing logging configuration 
Tell me more
What is the issue?
No logging configuration is set up for the server application.
Why this matters
Without proper logging configuration, the application lacks consistent log formatting, log levels, and potential output handlers needed for production monitoring.
Suggested change ∙ Feature Preview
def main():
"""Run the code analysis API server."""
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)Provide feedback to improve future suggestions
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| print(f"Starting code analysis API server on {args.host}:{args.port}") | ||
| print("API documentation available at http://localhost:8000/docs") |
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Server startup using print instead of logging
Tell me more
What is the issue?
Server startup information is using print statements instead of proper logging mechanisms.
Why this matters
Print statements don't provide timestamp, log level, or other contextual information needed for proper server monitoring and troubleshooting in production environments.
Suggested change ∙ Feature Preview
import logging
logging.info(f"Starting code analysis API server on {args.host}:{args.port}")
logging.info("API documentation available at http://localhost:8000/docs")Provide feedback to improve future suggestions
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| print(f"Starting code analysis API server on {args.host}:{args.port}") | ||
| print("API documentation available at http://localhost:8000/docs") | ||
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| uvicorn.run(app, host=args.host, port=args.port, reload=args.reload) |
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Missing error handling for uvicorn startup 
Tell me more
What is the issue?
The uvicorn.run() call can raise exceptions (e.g., port already in use, permission issues) but these exceptions are not caught or handled.
Why this matters
If uvicorn fails to start, the application will crash without any helpful error messages, making it difficult to diagnose deployment issues.
Suggested change ∙ Feature Preview
try:
uvicorn.run(app, host=args.host, port=args.port, reload=args.reload)
except OSError as e:
print(f"Failed to start server: {e}")
sys.exit(1)
except Exception as e:
print(f"Unexpected error while starting server: {e}")
sys.exit(1)Provide feedback to improve future suggestions
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| def main(): | ||
| """Run the code analysis API server.""" | ||
| parser = argparse.ArgumentParser(description="Run the code analysis API server") | ||
| parser.add_argument("--host", default="0.0.0.0", help="Host to bind the server to") |
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Overly permissive default network binding 
Tell me more
What is the issue?
The server is configured to bind to all network interfaces (0.0.0.0) by default, which exposes the API server to all network interfaces including external ones.
Why this matters
This default configuration could unintentionally expose the API server to external networks, potentially allowing unauthorized access from any IP address if not properly secured behind a firewall.
Suggested change ∙ Feature Preview
Change the default host to 'localhost' or '127.0.0.1' to only allow local connections by default:
parser.add_argument("--host", default="127.0.0.1", help="Host to bind the server to")Provide feedback to improve future suggestions
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| args = parser.parse_args() | ||
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| print(f"Starting code analysis API server on {args.host}:{args.port}") | ||
| print("API documentation available at http://localhost:8000/docs") |
There was a problem hiding this comment.
Hardcoded Documentation URL 
Tell me more
What is the issue?
The API documentation URL is hardcoded to localhost:8000 even though the host and port are configurable.
Why this matters
Users accessing the API from a different host or port will receive incorrect documentation URL information.
Suggested change ∙ Feature Preview
Use the configured host and port values in the documentation URL message:
print(f"API documentation available at http://{args.host}:{args.port}/docs")Provide feedback to improve future suggestions
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| @@ -0,0 +1,27 @@ | |||
| """ | |||
| Server script for running the code analysis API. | |||
There was a problem hiding this comment.
Insufficient module docstring context 
Tell me more
What is the issue?
The module docstring is too vague and doesn't explain why this server exists or its key features.
Why this matters
Without understanding the purpose and key capabilities, developers may misuse the server or struggle to maintain it effectively.
Suggested change ∙ Feature Preview
"""FastAPI server providing code analysis capabilities via REST API endpoints.
This server exposes code analysis features through a REST API, making them accessible
for integration with other tools and services. It uses uvicorn as the ASGI server.
"""
Provide feedback to improve future suggestions
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| def main(): | ||
| """Run the code analysis API server.""" |
There was a problem hiding this comment.
Non-informative function docstring
Tell me more
What is the issue?
The main() function docstring just restates what's obvious from the code.
Why this matters
The docstring should explain configuration options and any important runtime behavior.
Suggested change ∙ Feature Preview
"""Start the analysis server with the specified configuration.
The server supports configuration of host, port, and auto-reload functionality.
Swagger documentation is automatically available at /docs endpoint."""
Provide feedback to improve future suggestions
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| repo_url = sys.argv[1] | ||
| branch = sys.argv[2] if len(sys.argv) > 2 else None |
There was a problem hiding this comment.
Unsanitized Command Line Input
Tell me more
What is the issue?
Command line arguments (repo_url and branch) are used without any input validation or sanitization before being passed to Codebase.from_repo()
Why this matters
Malicious repository URLs or branch names could be used to execute arbitrary code or trigger security vulnerabilities during repository cloning/analysis.
Suggested change ∙ Feature Preview
def validate_repo_url(url: str) -> bool:
"""Validate repository URL format and allowed domains."""
# Add validation logic here
pass
def validate_branch_name(branch: str) -> bool:
"""Validate git branch name format."""
# Add validation logic here
pass
# In main():
if not validate_repo_url(repo_url):
raise ValueError("Invalid repository URL")
if branch and not validate_branch_name(branch):
raise ValueError("Invalid branch name")Provide feedback to improve future suggestions
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| try: | ||
| analyze_repo(repo_url, branch) | ||
| except Exception as e: | ||
| print(f"Error analyzing repository: {e}") |
There was a problem hiding this comment.
Over-broad exception handling
Tell me more
What is the issue?
Using a bare Exception catch-all with minimal error handling that only prints the error message.
Why this matters
Generic exception handling loses critical debugging information like stack traces and makes it harder to diagnose and fix specific issues.
Suggested change ∙ Feature Preview
try:
analyze_repo(repo_url, branch)
except Exception as e:
print(f"Error analyzing repository: {e}", file=sys.stderr)
print(f"Exception type: {type(e).__name__}", file=sys.stderr)
import traceback
traceback.print_exc()
sys.exit(1)Provide feedback to improve future suggestions
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| if error_analysis['errors']: | ||
| print("\nSample errors:") | ||
| for i, error in enumerate(error_analysis['errors'][:5]): # Show first 5 errors |
There was a problem hiding this comment.
Silent error truncation
Tell me more
What is the issue?
Silently truncating errors list without indicating there may be more errors.
Why this matters
Users may miss critical errors if they're not aware that only the first 5 errors are being shown.
Suggested change ∙ Feature Preview
if error_analysis['errors']:
print("\nSample errors (showing first 5 of {total}):"
.format(total=len(error_analysis['errors'])))
for i, error in enumerate(error_analysis['errors'][:5]):Provide feedback to improve future suggestions
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Enhanced Code Analysis Module
This PR enhances the code analysis module to provide a robust, full-featured code analysis server that properly utilizes
codebase_analysis.pyandcodebase_context.pyas requested.Key Features Added:
Error Detection System
Function Call Analysis
Type Validation
API Server
Example Script and Documentation
Implementation Details:
CodeAnalyzerclasscodebase_analysis.pyandcodebase_context.pyThis enhancement provides a complete solution for analyzing codebases, identifying issues, and suggesting fixes, making it much easier to maintain high code quality.
💻 View my work • About Codegen
Summary by Sourcery
Enhance the code analysis module with robust error detection, function call analysis, and type validation capabilities
New Features:
Enhancements:
Documentation:
Tests: