Enhance analysis.py with better CodebaseContext integration #18
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Reviewer's GuideThis pull request enhances the Sequence Diagram for Lazy Context InitializationsequenceDiagram
participant Client
participant Analysis
participant Codebase
participant ProjectConfig
participant CodebaseConfig
participant CodebaseContext
Client->>Analysis: access context property
Analysis->>Analysis: check _initialized
alt Not Initialized
Analysis->>Analysis: initialize()
Analysis->>Analysis: check _context
alt Context is None
Analysis->>Analysis: _create_context()
Analysis->>Codebase: access ctx attribute
alt Codebase has context
Codebase-->>Analysis: return existing context
else Codebase has no context
Analysis->>Codebase: access repo_operator, programming_language, base_path
Codebase-->>Analysis: return properties
Analysis->>ProjectConfig: new(repo_operator, ...)
ProjectConfig-->>Analysis: return project_config instance
Analysis->>CodebaseConfig: new()
CodebaseConfig-->>Analysis: return codebase_config instance
Analysis->>CodebaseContext: new([project_config], config=codebase_config)
CodebaseContext-->>Analysis: return new_context
end
Analysis->>Analysis: set _context = returned_context
end
Analysis->>Analysis: set _initialized = True
end
Analysis-->>Client: return _context
Class Diagram for Updated Analysis ClassclassDiagram
class Analysis {
-codebase: Codebase
-_context: CodebaseContext
-_initialized: bool
+__init__(codebase: Codebase)
+initialize()
-_create_context() CodebaseContext
+context() CodebaseContext
+get_codebase_summary() str
+get_symbol_summary(symbol_name: str) str
+find_symbol_by_name(symbol_name: str) Optional[Symbol]
+find_file_by_path(file_path: str) Optional[SourceFile]
+find_class_by_name(class_name: str) Optional[Class]
+find_function_by_name(function_name: str) Optional[Function]
+document_functions()
+get_extended_symbol_context(symbol_name: str, degree: int) Dict
+get_symbol_dependencies(symbol_name: str) List[str]
+get_symbol_usages(symbol_name: str) List[str]
+get_file_imports(file_path: str) List[str]
+get_file_exports(file_path: str) List[str]
+analyze_complexity() Dict[str, Any]
+get_file_dependencies(file_path: str) Dict[str, List[str]]
+get_codebase_structure() Dict[str, Any]
+get_monthly_commit_activity() Dict[str, int]
+get_file_change_frequency(limit: int) Dict[str, int]
}
note for Analysis "New methods added for finding elements (find_*), analyzing dependencies (get_*_dependencies, get_file_imports/exports), structure (get_codebase_structure), and history (get_*_activity/frequency).\nContext handling improved with lazy initialization.\nanalyze_complexity() significantly expanded."
class CodebaseContext
class Codebase
class Symbol
class SourceFile
class Class
class Function
Analysis --> Codebase : uses
Analysis --> CodebaseContext : uses / creates
Analysis ..> Symbol : finds
Analysis ..> SourceFile : finds
Analysis ..> Class : finds
Analysis ..> Function : finds
File-Level Changes
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Code Review
This PR introduces significant enhancements to the analysis module, providing new analysis capabilities and improving existing ones. The integration with CodebaseContext appears well-structured, and the addition of methods for finding symbols, files, classes, and functions enhances the module's utility. The code quality improvements, including refactoring and enhanced complexity analysis, are also valuable additions. However, there are a few areas that could benefit from further attention.
Summary of Findings
- Context Initialization: The lazy initialization of
CodebaseContextis a good approach, but ensure that all potential access points to the context are properly guarded to prevent uninitialized access. Consider adding a check within the_create_contextmethod to ensure that the necessary dependencies are available before proceeding with context creation. - Error Handling in Analysis Methods: Several analysis methods, such as
get_monthly_commit_activityandget_file_change_frequency, include try-except blocks that catch exceptions and return a dictionary with an 'error' key. While this prevents crashes, it might be better to log the exceptions and return more informative error messages or consider raising custom exceptions that can be handled upstream. This would provide more context to the caller about the nature of the failure. - Inconsistent use of
hasattr: The code useshasattrin several places to check for the existence of attributes before accessing them. While this is a valid approach, it's used inconsistently. For example, inget_file_exports,hasattris used to check forsymbol.is_exportedandsymbol.modifiers, but in other methods, direct attribute access is used. Consider standardizing the use ofhasattror usinggetattrwith a default value to handle missing attributes more gracefully.
Merge Readiness
The PR is a significant step forward for the analysis module. However, before merging, I recommend addressing the context initialization, error handling, and hasattr usage inconsistencies. These changes will improve the robustness and maintainability of the code. I am unable to directly approve this pull request, and recommend that others review and approve this code before merging.
| # If the codebase already has a context, use it | ||
| if hasattr(self.codebase, "ctx") and self.codebase.ctx is not None: | ||
| return self.codebase.ctx |
There was a problem hiding this comment.
This check is good for re-using an existing context. However, consider adding a check within this block to ensure that the existing context is still valid and contains the necessary dependencies (e.g., repo_operator, programming_language, base_path). If any of these are missing or invalid, it might be better to create a new context instead of re-using a potentially broken one.
| except Exception as e: | ||
| results["dependency_metrics"] = {"error": str(e)} |
There was a problem hiding this comment.
Catching all exceptions and returning {"error": str(e)} is a bit too broad. It would be better to catch specific exceptions that you anticipate (e.g., nx.NetworkXNoPath) and handle them accordingly. For other unexpected exceptions, consider logging them with more details (e.g., traceback) and potentially raising a custom exception that can be handled upstream.
| except Exception as e: | ||
| return {"error": str(e)} |
There was a problem hiding this comment.
Similar to the previous comment, catching all exceptions here is not ideal. It would be better to catch specific exceptions related to the repository operations (e.g., git.exc.GitCommandError) and handle them appropriately. Also, consider logging the exception with more details for debugging purposes.
PR Reviewer Guide 🔍(Review updated until commit 97157ab)Here are some key observations to aid the review process:
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Persistent review updated to latest commit 97157ab |
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I'll review PR #18 "Enhance analysis.py with better CodebaseContext integration" right away. Let me examine the changes and provide feedback. |
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PR DescriptionThis pull request enhances the Click to see moreKey Technical ChangesKey changes include: 1) Adding an initialization mechanism to manage the Architecture DecisionsThe architectural decisions involve extending the Dependencies and InteractionsThis pull request introduces dependencies on Risk ConsiderationsPotential risks include: 1) Performance overhead due to the construction of the dependency graph, especially for large codebases. 2) Increased memory usage due to caching and graph data structures. 3) Potential security vulnerabilities related to file path handling in Notable Implementation DetailsNotable implementation details include: 1) The use of |
Code ReviewI've reviewed the changes to enhance the Strengths
Suggestions for Improvement
Overall, this is a solid enhancement that significantly improves the analysis capabilities of the module. The changes are well-structured and maintain good code quality while adding valuable new functionality. |
![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 splitting the
CodebaseAnalyzerclass into smaller, more focused classes (e.g., one for structural analysis, one for metric calculation, one for git history analysis) to improve cohesion. - Review the error handling strategy in new methods like
get_monthly_commit_activity; returning error dictionaries might mask issues compared to raising exceptions.
Here's what I looked at during the review
- 🟡 General issues: 3 issues 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.
| # Initialize context if not already done | ||
| if self._context is None: | ||
| self._context = self._create_context() |
There was a problem hiding this comment.
suggestion (bug_risk): Consider error handling for context creation.
Wrap the _create_context() call in a try/except or verify its return value to handle any unexpected failures.
| # Initialize context if not already done | |
| if self._context is None: | |
| self._context = self._create_context() | |
| # Initialize context if not already done | |
| if self._context is None: | |
| try: | |
| self._context = self._create_context() | |
| except Exception as e: | |
| raise RuntimeError("Failed to create context") from e |
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| return structure | ||
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| def get_monthly_commit_activity(self) -> Dict[str, int]: |
There was a problem hiding this comment.
suggestion: Extract common commit period calculation into a helper.
Extract the one-year date range calculation into a shared helper to reduce duplication and improve maintainability.
Suggested implementation:
from datetime import timedelta
# Ensure UTC is imported already from your timezone module
def get_one_year_date_range():
"""
Returns:
A tuple (start_date, end_date) where end_date is the current datetime in UTC and start_date is one year prior.
"""
end_date = datetime.now(UTC)
start_date = end_date - timedelta(days=365)
return start_date, end_date
# (Rest of the file code) # Get commits from the last year using a shared helper
start_date, end_date = get_one_year_date_range()
commits = self.codebase.repo_operator.get_commits(start_date=start_date, end_date=end_date)Place the helper function at a location in the file that is common to consumers (e.g. right after your other helper functions or the import section) to ensure it’s available to all methods that need it.
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| # Process all files | ||
| for file in self.codebase.files: | ||
| path_parts = file.name.split('/') |
There was a problem hiding this comment.
suggestion: Consider using file path over file name for structure keys.
Splitting file.name by '/' assumes a relative path; using file.path handles both relative and absolute paths more robustly and aligns with other code.
| path_parts = file.name.split('/') | |
| path_parts = file.path.split('/') |
| return get_symbol_summary(symbol) | ||
| return f"Symbol not found: {symbol_name}" | ||
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| def find_symbol_by_name(self, symbol_name: str) -> Optional[Symbol]: |
There was a problem hiding this comment.
issue (complexity): Consider using helper functions to encapsulate repeated lookup logic and nested structure building to simplify the code.
You might simplify the repeated lookup logic and deep nested loops by offloading some responsibilities into helper functions. For example:
-
Factor Lookups:
Instead of duplicating loops in eachfind_*method, you can create a generic lookup helper:def find_by(collection, attr, value): for item in collection: if getattr(item, attr, None) == value: return item return None # Usage in the CodeAnalyzer class: def find_symbol_by_name(self, symbol_name: str) -> Optional[Symbol]: return find_by(self.codebase.symbols, 'name', symbol_name)
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Refactor Nested Structure Building:
Extract the nested loop ofget_codebase_structureinto a helper function that recursively builds the directory tree:def add_to_structure(structure: dict, path_parts: List[str], file_info: dict) -> None: current = structure for part in path_parts[:-1]: current = current.setdefault(part, {}) current[path_parts[-1]] = file_info # Then in your get_codebase_structure: def get_codebase_structure(self) -> Dict[str, Any]: structure = {} for file in self.codebase.files: file_info = { "type": "file", "symbols": [{"name": symbol.name, "type": str(getattr(symbol, "symbol_type", "unknown"))} for symbol in file.symbols] } add_to_structure(structure, file.name.split('/'), file_info) return structure
These refactorings encapsulate repeated logic and can reduce the complexity of analysis.py without changing its functionality.
| for symbol in self.codebase.symbols: | ||
| if symbol.name == symbol_name: | ||
| return symbol | ||
| return None |
There was a problem hiding this comment.
suggestion (code-quality): Use the built-in function next instead of a for-loop (use-next)
| for symbol in self.codebase.symbols: | |
| if symbol.name == symbol_name: | |
| return symbol | |
| return None | |
| return next( | |
| ( | |
| symbol | |
| for symbol in self.codebase.symbols | |
| if symbol.name == symbol_name | |
| ), | |
| None, | |
| ) |
| for func in self.codebase.functions: | ||
| if func.name == function_name: | ||
| return func | ||
| return None |
There was a problem hiding this comment.
suggestion (code-quality): Use the built-in function next instead of a for-loop (use-next)
| for func in self.codebase.functions: | |
| if func.name == function_name: | |
| return func | |
| return None | |
| return next( | |
| ( | |
| func | |
| for func in self.codebase.functions | |
| if func.name == function_name | |
| ), | |
| None, | |
| ) |
| symbol = self.find_symbol_by_name(symbol_name) | ||
| if symbol: |
There was a problem hiding this comment.
suggestion (code-quality): Use named expression to simplify assignment and conditional (use-named-expression)
| symbol = self.find_symbol_by_name(symbol_name) | |
| if symbol: | |
| if symbol := self.find_symbol_by_name(symbol_name): |
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| return exports | ||
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| def analyze_complexity(self) -> Dict[str, Any]: |
There was a problem hiding this comment.
issue (code-quality): We've found these issues:
- Extract code out into method (
extract-method) - Low code quality found in CodeAnalyzer.analyze_complexity - 18% (
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.
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| try: | ||
| # Get commits from the last year | ||
| end_date = datetime.now(UTC) |
There was a problem hiding this comment.
issue (code-quality): Extract code out into method (extract-method)
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| try: | ||
| # Get commits from the last year | ||
| end_date = datetime.now(UTC) |
There was a problem hiding this comment.
issue (code-quality): Extract code out into method (extract-method)
![codegen-sh[bot]](https://avatars.githubusercontent.com/in/321279?s=60&v=4){kind=small}
| """ | ||
| # If the codebase already has a context, use it | ||
| if hasattr(self.codebase, "ctx") and self.codebase.ctx is not None: | ||
| return self.codebase.ctx |
There was a problem hiding this comment.
The context validation suggestion applies here. Consider adding a check to verify that the existing context has the necessary attributes and configuration before reusing it. For example:
# If the codebase already has a context, validate and use it
if hasattr(self.codebase, "ctx") and self.codebase.ctx is not None:
ctx = self.codebase.ctx
# Validate that the context has necessary attributes
if (hasattr(ctx, "project_configs") and
hasattr(ctx, "config") and
ctx.project_configs):
return ctx
# If validation fails, log a warning and create a new contextThis would prevent issues if the existing context is incomplete or improperly configured.
| "most_central": most_central | ||
| } | ||
| except Exception as e: | ||
| results["dependency_metrics"] = {"error": str(e)} |
There was a problem hiding this comment.
The error handling here is quite broad. Consider catching specific exceptions that might occur during graph analysis, such as nx.NetworkXError or nx.NetworkXNoPath. This would allow for more informative error messages and potentially better recovery strategies.
try:
in_degree_centrality = nx.in_degree_centrality(dependency_graph)
out_degree_centrality = nx.out_degree_centrality(dependency_graph)
betweenness_centrality = nx.betweenness_centrality(dependency_graph)
# Find most central symbols
most_imported = sorted(in_degree_centrality.items(), key=lambda x: x[1], reverse=True)[:10]
most_dependent = sorted(out_degree_centrality.items(), key=lambda x: x[1], reverse=True)[:10]
most_central = sorted(betweenness_centrality.items(), key=lambda x: x[1], reverse=True)[:10]
results["dependency_metrics"] = {
"most_imported": most_imported,
"most_dependent": most_dependent,
"most_central": most_central
}
except nx.NetworkXError as e:
results["dependency_metrics"] = {"error": f"NetworkX error: {str(e)}"}
except ValueError as e:
results["dependency_metrics"] = {"error": f"Value error: {str(e)}"}
except Exception as e:
# Log the unexpected exception with more details
results["dependency_metrics"] = {"error": f"Unexpected error: {str(e)}"}
| """ | ||
| for symbol in self.codebase.symbols: | ||
| if symbol.name == symbol_name: | ||
| return get_symbol_summary(symbol) |
There was a problem hiding this comment.
For large codebases, these linear searches through all symbols could become a performance bottleneck. Consider implementing a caching mechanism or an index for faster lookups. For example:
def initialize(self):
"""
Initialize the analyzer by setting up the context and other necessary components.
This is called automatically when needed but can be called explicitly for eager initialization.
"""
if self._initialized:
return
# Initialize context if not already done
if self._context is None:
self._context = self._create_context()
# Build indices for faster lookups
self._symbol_index = {symbol.name: symbol for symbol in self.codebase.symbols}
self._class_index = {cls.name: cls for cls in self.codebase.classes}
self._function_index = {func.name: func for func in self.codebase.functions}
self._initialized = True
def find_symbol_by_name(self, symbol_name: str) -> Optional[Symbol]:
"""
Find a symbol by its name.
Args:
symbol_name: Name of the symbol to find
Returns:
The Symbol object if found, None otherwise
"""
if not self._initialized:
self.initialize()
return self._symbol_index.get(symbol_name)This would significantly improve lookup performance for repeated operations.
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I've completed my review and didn't find any issues... but I did find this rooster.
\\
(o>
\\_//)
\_/_)
_|_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.
| A dictionary representing the codebase structure | ||
| """ | ||
| # Initialize the structure with root directories | ||
| structure = {} |
There was a problem hiding this comment.
The get_codebase_structure method creates a useful hierarchical representation, but it might be helpful to add a parameter to control the depth of the hierarchy or to filter by specific file types. This would make the method more flexible for different use cases, especially for large codebases where you might only want a partial view.
def get_codebase_structure(self, max_depth: int = None, file_extensions: List[str] = None) -> Dict[str, Any]:
"""
Get a hierarchical representation of the codebase structure.
Args:
max_depth: Maximum depth of the directory hierarchy to include (None for unlimited)
file_extensions: List of file extensions to include (None for all files)
Returns:
A dictionary representing the codebase structure
"""
# Initialize the structure with root directories
structure = {}
# Process all files
for file in self.codebase.files:
# Filter by file extension if specified
if file_extensions and not any(file.name.endswith(ext) for ext in file_extensions):
continue
path_parts = file.name.split('/')
# Apply max_depth filter if specified
if max_depth is not None and len(path_parts) > max_depth:
path_parts = path_parts[:max_depth]
current = structure
# Build the directory structure
for i, part in enumerate(path_parts[:-1]):
if part not in current:
current[part] = {}
current = current[part]
# Add the file with its symbols
# ... rest of the implementation ...
| "average": avg_complexity, | ||
| "rank": cc_rank(avg_complexity), | ||
| "functions": complexity_results | ||
| "functions": complexity_results, |
There was a problem hiding this comment.
In the analyze_complexity method, the cyclomatic complexity results no longer include the rank field that was present in the original code. This might be an oversight, as the rank provides a useful qualitative assessment of the complexity. Consider adding it back:
results["cyclomatic_complexity"] = {
"functions": complexity_results,
"average": avg_complexity,
"rank": cc_rank(avg_complexity) # Add this back
}This would maintain backward compatibility and provide the qualitative assessment that was previously available.
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I've completed a thorough review of PR #18 "Enhance analysis.py with better CodebaseContext integration". Overall, this is a well-structured and valuable enhancement to the codebase analysis capabilities. The changes improve the initialization logic, add useful helper methods, and significantly expand the analysis capabilities. I've provided:
Key recommendations include:
The PR is well-designed and ready to merge after addressing these minor suggestions. Great work on enhancing the analysis module! 💻 View my work • React 👍 or 👎 |
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✅ Fixed mypy type annotation issues in analysis.py in this commit. The issue was missing type annotations for functions and methods in the analysis.py file. |
User description
Enhanced Analysis Module
This PR enhances the
analysis.pymodule to better integrate withcodebase_context.pyandcodebase_analysis.py. The changes include:Improved CodebaseContext Integration
New Analysis Capabilities
Code Quality Improvements
These changes make the analysis module more powerful and easier to use, providing a more comprehensive set of tools for codebase analysis.
💻 View my work • About Codegen
Summary by Sourcery
Enhance the analysis module with comprehensive codebase analysis capabilities, improving CodebaseContext integration and adding advanced analysis methods
New Features:
Enhancements:
Chores:
PR Type
Enhancement
Description
Improved CodebaseContext integration with lazy initialization and context creation.
Added advanced symbol, file, class, and function search methods.
Introduced comprehensive codebase analysis capabilities:
Refactored and extended existing methods for robustness and clarity.
Changes walkthrough 📝
analysis.py
Major analysis enhancements and context integration improvementscodegen-on-oss/codegen_on_oss/analysis/analysis.py
CodebaseContext.
name.
and file dependencies.
analytics.
dependency graph metrics.
extensibility.
Description by Korbit AI
What change is being made?
Integrate a more comprehensive
CodebaseContextintoanalysis.pyalong with enhancements that enable the analysis of symbols, files, classes, and functions within the codebase, including complexity, dependency, and commit analytics.Why are these changes being made?
These changes provide a richer context and more granular analysis capabilities for the codebase, which helps in understanding dependencies and structure. The improved initialization of the
CodebaseContextallows for more consistent setup and enhances the overall performance of operations due to its eager initialization. Additionally, new analytical methods provide insights into code complexity, usage, and historical changes, which are crucial for developers to maintain and improve the codebase over time.