Enhance analysis.py with better CodebaseContext and codebase_analysis integration #19
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Create fully interconnected analysis module with comprehensive metrics integration
Enhance analysis.py with better CodebaseContext integration
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Code Review
This PR enhances the analysis.py module with better integration of CodebaseContext and codebase_analysis functionality, providing more comprehensive and accurate code analysis results. The changes include improvements to the CodeAnalyzer class, addition of new methods utilizing CodebaseContext capabilities, and new API endpoints. Overall, the changes seem well-structured and contribute to the enhancement of the analysis module.
Summary of Findings
- Missing Error Handling: In
get_symbol_dependencies, the function returns a dictionary with an 'error' key containing a list. However, the API endpoint/analyze_symboldoes not handle this error case, potentially leading to unexpected behavior or crashes if the symbol is not found. The endpoint should check for the 'error' key and return an appropriate error response to the client. - Inconsistent use of
hasattr: The code useshasattrin several places to check for the existence of attributes before accessing them. While this is a good practice, it's not consistently applied across all functions and methods. For example, inget_context_for_symbol, the code checkshasattr(symbol, "symbol_type")but nothasattr(symbol, "file"). Consistency in usinghasattrwould improve the robustness of the code. - Potential Performance Bottleneck: In
get_context_for_symbolandget_file_dependencies, the code iterates throughctx.nodesto find the node ID. This is an O(n) operation, which could become a performance bottleneck for large codebases. Consider using a dictionary to map symbol/file names to node IDs for O(1) lookup.
Merge Readiness
The pull request introduces significant enhancements to the analysis module. However, the missing error handling in get_symbol_dependencies and the potential performance bottleneck in get_context_for_symbol and get_file_dependencies should be addressed before merging. I am unable to directly approve this pull request, and recommend that others review and approve this code before merging. At a minimum, the high severity issues should be addressed before merging.
| symbol = self.find_symbol_by_name(symbol_name) | ||
| if symbol is None: |
There was a problem hiding this comment.
The function returns a dictionary with an 'error' key containing a list. However, the API endpoint /analyze_symbol does not handle this error case, potentially leading to unexpected behavior or crashes if the symbol is not found. The endpoint should check for the 'error' key and return an appropriate error response to the client.
| for n_id, node in enumerate(ctx.nodes): | ||
| if isinstance(node, Symbol) and node.name == symbol_name: | ||
| node_id = n_id | ||
| break |
There was a problem hiding this comment.
Consider using a dictionary to map symbol names to node IDs for O(1) lookup instead of iterating through all nodes. This could improve performance for large codebases.
| "type": symbol.symbol_type.name if hasattr(symbol, "symbol_type") else "Unknown", | ||
| "file": symbol.file.name if hasattr(symbol, "file") else "Unknown" |
There was a problem hiding this comment.
It's good that you're checking hasattr(symbol, "symbol_type"), but you should also check hasattr(symbol, "file") for consistency and to avoid potential errors if the file attribute is missing.
| for n_id, node in enumerate(ctx.nodes): | ||
| if isinstance(node, SourceFile) and node.name == file.name: | ||
| node_id = n_id | ||
| break |
There was a problem hiding this comment.
Consider using a dictionary to map file names to node IDs for O(1) lookup instead of iterating through all nodes. This could improve performance for large codebases.
PR Reviewer Guide 🔍(Review updated until commit 7d563cf)Here are some key observations to aid the review process:
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PR DescriptionThis pull request refactors the Click to see moreKey Technical ChangesThe primary technical changes include: 1) Replacing several methods in Architecture DecisionsThe key architectural decision is to centralize dependency and relationship information within the Dependencies and InteractionsThis pull request depends on the Risk ConsiderationsPotential risks include: 1) The refactoring might introduce regressions if the Notable Implementation DetailsNotable implementation details include: 1) The use of |
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| Returns: | ||
| A dictionary containing import analysis results | ||
| """ | ||
| graph = create_graph_from_codebase(self.codebase.repo_name) | ||
| graph = create_graph_from_codebase(self.codebase) | ||
| cycles = find_import_cycles(graph) | ||
| problematic_loops = find_problematic_import_loops(graph, cycles) | ||
| problematic_loops = find_problematic_import_loops(graph) | ||
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| return { | ||
| "import_cycles": cycles, | ||
| "import_graph": graph, | ||
| "cycles": cycles, | ||
| "problematic_loops": problematic_loops |
There was a problem hiding this comment.
The analyze_imports method now takes self.codebase as a parameter to create_graph_from_codebase instead of self.codebase.repo_name, but find_problematic_import_loops no longer receives the cycles parameter. This could lead to inefficiency as the cycles might need to be recalculated. Consider passing the already computed cycles to avoid duplicate work.
| Returns: | |
| A dictionary containing import analysis results | |
| """ | |
| graph = create_graph_from_codebase(self.codebase.repo_name) | |
| graph = create_graph_from_codebase(self.codebase) | |
| cycles = find_import_cycles(graph) | |
| problematic_loops = find_problematic_import_loops(graph, cycles) | |
| problematic_loops = find_problematic_import_loops(graph) | |
| return { | |
| "import_cycles": cycles, | |
| "import_graph": graph, | |
| "cycles": cycles, | |
| "problematic_loops": problematic_loops | |
| def analyze_imports(self) -> Dict[str, Any]: | |
| graph = create_graph_from_codebase(self.codebase) | |
| cycles = find_import_cycles(graph) | |
| problematic_loops = find_problematic_import_loops(graph, cycles) | |
| return { | |
| "import_graph": graph, | |
| "cycles": cycles, | |
| "problematic_loops": problematic_loops | |
| } |
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| def convert_args_to_kwargs(self) -> None: | ||
| """ | ||
| Convert all function call arguments to keyword arguments. | ||
| """ | ||
| convert_all_calls_to_kwargs(self.codebase) | ||
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| def visualize_module_dependencies(self) -> None: | ||
| """ | ||
| Visualize module dependencies in the codebase. | ||
| """ | ||
| module_dependencies_run(self.codebase) | ||
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| def generate_mdx_documentation(self, class_name: str) -> str: | ||
| def analyze_complexity(self) -> Dict[str, Any]: | ||
| """ | ||
| Generate MDX documentation for a class. | ||
| Analyze code complexity metrics for the codebase. | ||
| Args: | ||
| class_name: Name of the class to document | ||
| Returns: | ||
| MDX documentation as a string | ||
| """ | ||
| for cls in self.codebase.classes: | ||
| if cls.name == class_name: | ||
| return render_mdx_page_for_class(cls) | ||
| return f"Class not found: {class_name}" | ||
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| def print_symbol_attribution(self) -> None: | ||
| """ | ||
| Print attribution information for symbols in the codebase. | ||
| A dictionary containing complexity metrics | ||
| """ | ||
| print_symbol_attribution(self.codebase) | ||
| # Calculate cyclomatic complexity for all functions | ||
| complexity_results = {} | ||
| for func in self.codebase.functions: | ||
| if hasattr(func, "code_block"): | ||
| complexity = calculate_cyclomatic_complexity(func) | ||
| complexity_results[func.name] = { | ||
| "complexity": complexity, | ||
| "rank": cc_rank(complexity) | ||
| } | ||
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| # Calculate line metrics for all files | ||
| line_metrics = {} | ||
| for file in self.codebase.files: | ||
| if hasattr(file, "source"): | ||
| loc, lloc, sloc, comments = count_lines(file.source) | ||
| line_metrics[file.name] = { | ||
| "loc": loc, | ||
| "lloc": lloc, | ||
| "sloc": sloc, | ||
| "comments": comments | ||
| } | ||
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| return { | ||
| "cyclomatic_complexity": complexity_results, |
There was a problem hiding this comment.
The analyze_complexity method could benefit from error handling for the case where functions don't have the expected attributes. Currently, it assumes all functions will have a code_block attribute. Consider adding try-except blocks or validation to handle potential attribute errors gracefully.
| def convert_args_to_kwargs(self) -> None: | |
| """ | |
| Convert all function call arguments to keyword arguments. | |
| """ | |
| convert_all_calls_to_kwargs(self.codebase) | |
| def visualize_module_dependencies(self) -> None: | |
| """ | |
| Visualize module dependencies in the codebase. | |
| """ | |
| module_dependencies_run(self.codebase) | |
| def generate_mdx_documentation(self, class_name: str) -> str: | |
| def analyze_complexity(self) -> Dict[str, Any]: | |
| """ | |
| Generate MDX documentation for a class. | |
| Analyze code complexity metrics for the codebase. | |
| Args: | |
| class_name: Name of the class to document | |
| Returns: | |
| MDX documentation as a string | |
| """ | |
| for cls in self.codebase.classes: | |
| if cls.name == class_name: | |
| return render_mdx_page_for_class(cls) | |
| return f"Class not found: {class_name}" | |
| def print_symbol_attribution(self) -> None: | |
| """ | |
| Print attribution information for symbols in the codebase. | |
| A dictionary containing complexity metrics | |
| """ | |
| print_symbol_attribution(self.codebase) | |
| # Calculate cyclomatic complexity for all functions | |
| complexity_results = {} | |
| for func in self.codebase.functions: | |
| if hasattr(func, "code_block"): | |
| complexity = calculate_cyclomatic_complexity(func) | |
| complexity_results[func.name] = { | |
| "complexity": complexity, | |
| "rank": cc_rank(complexity) | |
| } | |
| # Calculate line metrics for all files | |
| line_metrics = {} | |
| for file in self.codebase.files: | |
| if hasattr(file, "source"): | |
| loc, lloc, sloc, comments = count_lines(file.source) | |
| line_metrics[file.name] = { | |
| "loc": loc, | |
| "lloc": lloc, | |
| "sloc": sloc, | |
| "comments": comments | |
| } | |
| return { | |
| "cyclomatic_complexity": complexity_results, | |
| def analyze_complexity(self) -> Dict[str, Any]: | |
| complexity_results = {} | |
| for func in self.codebase.functions: | |
| try: | |
| if hasattr(func, "code_block"): | |
| complexity = calculate_cyclomatic_complexity(func) | |
| complexity_results[func.name] = { | |
| "complexity": complexity, | |
| "rank": cc_rank(complexity) | |
| } | |
| except AttributeError as e: | |
| logger.warning(f"Could not calculate complexity for {func.name}: {e}") | |
| continue |
| } | ||
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| def get_extended_symbol_context(self, symbol_name: str, degree: int = 2) -> Dict[str, List[str]]: | ||
| def get_dependency_graph(self) -> nx.DiGraph: | ||
| """ | ||
| Get extended context (dependencies and usages) for a symbol. | ||
| Generate a dependency graph for the codebase. | ||
| Args: | ||
| symbol_name: Name of the symbol to analyze | ||
| degree: How many levels deep to collect dependencies and usages | ||
| Returns: | ||
| A dictionary containing dependencies and usages | ||
| A NetworkX DiGraph representing dependencies | ||
| """ | ||
| symbol = self.find_symbol_by_name(symbol_name) | ||
| if symbol: | ||
| dependencies, usages = get_extended_context(symbol, degree) | ||
| return { | ||
| "dependencies": [dep.name for dep in dependencies], | ||
| "usages": [usage.name for usage in usages] | ||
| } | ||
| return {"dependencies": [], "usages": []} | ||
| G = nx.DiGraph() | ||
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| # Add nodes for all files | ||
| for file in self.codebase.files: | ||
| G.add_node(file.name, type="file") | ||
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| # Add edges for imports | ||
| for file in self.codebase.files: | ||
| for imp in file.imports: | ||
| if imp.imported_symbol and hasattr(imp.imported_symbol, "file"): | ||
| imported_file = imp.imported_symbol.file | ||
| if imported_file and imported_file.name != file.name: | ||
| G.add_edge(file.name, imported_file.name) |
There was a problem hiding this comment.
The get_dependency_graph method returns a NetworkX DiGraph but doesn't include type hints from NetworkX in the imports. Also, the method could benefit from documentation about the expected node/edge attributes in the graph. Consider adding proper imports and detailed documentation.
| } | |
| def get_extended_symbol_context(self, symbol_name: str, degree: int = 2) -> Dict[str, List[str]]: | |
| def get_dependency_graph(self) -> nx.DiGraph: | |
| """ | |
| Get extended context (dependencies and usages) for a symbol. | |
| Generate a dependency graph for the codebase. | |
| Args: | |
| symbol_name: Name of the symbol to analyze | |
| degree: How many levels deep to collect dependencies and usages | |
| Returns: | |
| A dictionary containing dependencies and usages | |
| A NetworkX DiGraph representing dependencies | |
| """ | |
| symbol = self.find_symbol_by_name(symbol_name) | |
| if symbol: | |
| dependencies, usages = get_extended_context(symbol, degree) | |
| return { | |
| "dependencies": [dep.name for dep in dependencies], | |
| "usages": [usage.name for usage in usages] | |
| } | |
| return {"dependencies": [], "usages": []} | |
| G = nx.DiGraph() | |
| # Add nodes for all files | |
| for file in self.codebase.files: | |
| G.add_node(file.name, type="file") | |
| # Add edges for imports | |
| for file in self.codebase.files: | |
| for imp in file.imports: | |
| if imp.imported_symbol and hasattr(imp.imported_symbol, "file"): | |
| imported_file = imp.imported_symbol.file | |
| if imported_file and imported_file.name != file.name: | |
| G.add_edge(file.name, imported_file.name) | |
| from networkx import DiGraph | |
| def get_dependency_graph(self) -> DiGraph: | |
| """Generate a dependency graph for the codebase. | |
| Returns: | |
| A NetworkX DiGraph where: | |
| - Nodes represent files with attributes: {type: 'file'} | |
| - Edges represent imports between files | |
| - Each edge (a,b) means file 'a' imports from file 'b' | |
| """ | |
| G = DiGraph() |
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I've completed my review and didn't find any issues... but I did find this penguin.
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( o>
///\
\V_/_Files scanned
| File Path | Reviewed |
|---|---|
| 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.
PR DescriptionThis pull request refactors the code analysis capabilities to provide more granular and context-aware insights into a codebase. It aims to improve the accuracy and usefulness of the analysis results by leveraging the CodebaseContext and simplifying complexity calculations. Click to see moreKey Technical Changes
Architecture Decisions
Dependencies and Interactions
Risk Considerations
Notable Implementation Details
|
![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:
- Consider explicitly mentioning in the PR description where the removed analysis functionalities (like Halstead metrics, DOI, commit analysis) have been relocated or if they are deprecated.
- The structure and content returned by the
/analyze_repoendpoint have changed significantly; confirm this breaking change is intended and documented for API consumers.
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.
| dependency_graph = nx.DiGraph() | ||
| return print_symbol_attribution(symbol) | ||
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| def get_context_for_symbol(self, symbol_name: str) -> Dict[str, Any]: |
There was a problem hiding this comment.
issue (complexity): Consider building and caching a name-to-node index for context lookups to replace repeated manual iteration in methods like get_context_for_symbol and get_file_dependencies.
Consider caching a lookup index for context nodes to avoid the manual iteration in both methods. For example, you might build a dictionary mapping symbol or file names to their node IDs when the context is created:
def _build_context_index(self) -> Dict[str, int]:
return {node.name: i for i, node in enumerate(self.context.nodes) if hasattr(node, "name")}Then, in get_context_for_symbol, you can simply look up the node ID:
def get_context_for_symbol(self, symbol_name: str) -> Dict[str, Any]:
symbol = self.find_symbol_by_name(symbol_name)
if symbol is None:
return {"error": f"Symbol not found: {symbol_name}"}
ctx = self.context
context_index = self._build_context_index() # Or cache it
node_id = context_index.get(symbol_name)
if node_id is None:
return {"error": f"Symbol not found in context: {symbol_name}"}
predecessors = [
{"name": pred.name, "type": getattr(pred, 'symbol_type', 'Unknown').name}
for pred in ctx.predecessors(node_id)
if isinstance(pred, Symbol)
]
successors = [
{"name": succ.name, "type": getattr(succ, 'symbol_type', 'Unknown').name}
for succ in ctx.successors(node_id)
if isinstance(succ, Symbol)
]
return {
"symbol": {
"name": symbol.name,
"type": getattr(symbol, 'symbol_type', 'Unknown').name,
"file": getattr(symbol, 'file', 'Unknown').name
},
"predecessors": predecessors,
"successors": successors
}Apply a similar approach in get_file_dependencies by building an index keyed on file names. This refactoring will lower the cognitive load by eliminating nested iterations while keeping current functionality intact.
| return print_symbol_attribution(symbol) | ||
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| def get_context_for_symbol(self, symbol_name: str) -> Dict[str, Any]: | ||
| """ |
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) - Convert for loop into list comprehension [×2] (
list-comprehension)
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| def get_file_dependencies(self, file_path: str) -> Dict[str, List[str]]: | ||
| def get_file_dependencies(self, file_path: str) -> Dict[str, Any]: | ||
| """ |
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) - Convert for loop into list comprehension [×2] (
list-comprehension)
| edge_types[edge_type] = edge_types.get(edge_type, 0) + 1 | ||
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| # Get directories structure | ||
| directories = {} |
There was a problem hiding this comment.
issue (code-quality): Convert for loop into dictionary comprehension (dict-comprehension)
| structure_analysis = analyzer.analyze_codebase_structure() | ||
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| # Combine all results | ||
| results = { |
There was a problem hiding this comment.
issue (code-quality): Inline variable that is immediately returned (inline-immediately-returned-variable)
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update PR with codegen-on-oss/codegen_on_oss/analysis/analysis.py @sourcery-ai sourcery-ai bot 1 minute ago Consider caching a lookup index for context nodes to avoid the manual iteration in both methods. For example, you might build a dictionary mapping symbol or file names to their node IDs when the context is created: def _build_context_index(self) -> Dict[str, int]: def get_context_for_symbol(self, symbol_name: str) -> Dict[str, Any]: Apply a similar approach in get_file_dependencies by building an index keyed on file names. This refactoring will lower the cognitive load by eliminating nested iterations while keeping current functionality intact. @Zeeeepa Reply... @sourcery-ai sourcery-ai bot 1 minute ago Use the built-in function next instead of a for-loop (use-next) @sourcery-ai sourcery-ai bot 1 minute ago Use the built-in function next instead of a for-loop (use-next) @sourcery-ai sourcery-ai bot 1 minute ago @Zeeeepa Reply... |
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This PR enhances the
analysis.pymodule with better integration ofCodebaseContextandcodebase_analysisfunctionality.Changes:
Improved the
CodeAnalyzerclass to better leverage theCodebaseContextfor more advanced code analysis:analyze_imports()methodAdded new methods that utilize
CodebaseContextcapabilities:get_context_for_symbol(): Gets extended context information for a symbolget_file_dependencies(): Gets dependency information for a fileanalyze_codebase_structure(): Analyzes the overall structure of the codebaseAdded new API endpoints:
/analyze_symbol: For analyzing a symbol and its relationships/analyze_file: For analyzing a file and its relationshipsEnhanced the existing
/analyze_repoendpoint to include structure analysisThese changes make the analysis module more powerful by properly integrating the capabilities of the
CodebaseContextandcodebase_analysismodules, providing more comprehensive and accurate code analysis results.💻 View my work • About Codegen
Summary by Sourcery
Enhance the analysis module by integrating CodebaseContext and improving code analysis capabilities with more comprehensive and context-aware methods
New Features:
Enhancements:
Chores: