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Copilot AI commented Dec 16, 2025

Implementation Plan for Advanced Risk Assessment

  • Create a new risk assessment module (refactron/core/risk_assessment.py)
    • Implement multi-factor risk scoring system
    • Add impact scope calculator (how many parts of code affected)
    • Add change type classifier (renaming vs restructuring)
    • Add test coverage dependency analyzer
    • Add dependency impact analyzer
  • Extend RefactoringOperation model with detailed risk metrics
    • Add risk_factors field to metadata
    • Store impact_scope, change_type, test_coverage metrics
  • Update refactorers to use new risk assessment
    • Update MagicNumberRefactorer to use advanced risk scoring
    • Update ReduceParametersRefactorer to use advanced risk scoring
    • Update SimplifyConditionalsRefactorer to use advanced risk scoring
    • Update AddDocstringRefactorer to use advanced risk scoring
  • Implement risk visualization in reports
    • Update RefactorResult.show_diff() to include risk breakdown
    • Add visual risk indicators
  • Implement risk visualization in CLI output
    • Add helper function for color-coded risk display
    • Add risk level categorization with icons
  • Add comprehensive tests for risk assessment
    • Test multi-factor scoring
    • Test impact scope calculation
    • Test change type classification
    • Test dependency impact analysis
  • Run validation and testing
    • Run existing tests to ensure no regressions
    • Run new tests for risk assessment
    • Validate CLI output
    • Fix code review comments
    • Pass security checks
  • Address PR feedback
    • Improve test assertions for dependency impact analysis
    • Extract hardcoded risk cap constant
  • Fix CI failures
    • Format all code with Black
    • Sort imports with isort
  • Address second round of code review
    • Fix Python 3.8 compatibility (use Tuple instead of tuple)
    • Remove unused pytest import
    • Extract all magic numbers to class constants
    • Populate test_file_exists field in RiskFactors
  • Address third round of code review
    • Centralize risk display logic to eliminate duplication

Summary

Successfully implemented advanced risk assessment with multi-factor scoring. All 54 risk assessment tests pass with 89% coverage. Code is Python 3.8+ compatible with centralized risk display logic.

Original prompt

This section details on the original issue you should resolve

<issue_title>2.1 Advanced Risk Assessment</issue_title>
<issue_description>Implement multi-factor risk scoring: Impact scope (how many parts of code affected) Type of change (renaming vs restructuring) Test coverage dependency (risky if low test coverage) Add dependency impact analysis (what breaks if this refactoring is applied) Create risk visualization in reports and CLI output</issue_description>

<agent_instructions>Donot over write and donot write the unwanted comments just implement the functionality in a better way</agent_instructions>

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Copilot AI and others added 3 commits December 16, 2025 16:54
Co-authored-by: omsherikar <180152315+omsherikar@users.noreply.github.com>
Co-authored-by: omsherikar <180152315+omsherikar@users.noreply.github.com>
…ng parameters

Co-authored-by: omsherikar <180152315+omsherikar@users.noreply.github.com>
Copilot AI changed the title [WIP] Add multi-factor risk scoring implementation Implement multi-factor risk assessment for refactoring operations Dec 16, 2025
Copilot AI requested a review from omsherikar December 16, 2025 17:02
@omsherikar omsherikar marked this pull request as ready for review December 16, 2025 17:03
Copilot AI review requested due to automatic review settings December 16, 2025 17:03
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Pull request overview

This PR implements a comprehensive multi-factor risk assessment system for refactoring operations. The system evaluates five weighted risk factors (change type 30%, test coverage 25%, dependencies 20%, impact scope 15%, complexity 10%) to generate risk scores from 0.0 to 1.0, providing detailed breakdowns and visual indicators throughout the CLI and result displays.

Key Changes:

  • New RiskAssessor class with five-factor weighted risk calculation algorithm
  • Integration of risk assessment into all four refactorer types with appropriate change type classifications
  • Enhanced visualization with color-coded risk indicators and detailed factor breakdowns in CLI and result outputs

Reviewed changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 2 comments.

Show a summary per file
File Description
refactron/core/risk_assessment.py Core risk assessment module with weighted scoring, test coverage detection, dependency analysis, and impact scope calculation
refactron/refactorers/magic_number_refactorer.py Integrated risk assessment with EXTRACTION change type for constant extraction operations
refactron/refactorers/reduce_parameters_refactorer.py Added risk calculation with API_CHANGE type for parameter reduction operations
refactron/refactorers/simplify_conditionals_refactorer.py Implemented risk assessment with LOGIC_CHANGE type for control flow modifications
refactron/refactorers/add_docstring_refactorer.py Added risk calculation with RENAMING type, capped at 0.1 for documentation-only changes
refactron/core/refactor_result.py Enhanced diff display with risk icons and detailed factor breakdowns
refactron/cli.py Added color-coded risk visualization helper function with detailed breakdowns
tests/test_risk_assessment.py Comprehensive test suite with 20 tests covering all risk calculation paths
tests/test_refactorers.py Updated existing tests to accommodate realistic risk scores from advanced assessment

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…nstant

Co-authored-by: omsherikar <180152315+omsherikar@users.noreply.github.com>
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@copilot Some CI are failing fix them

Co-authored-by: omsherikar <180152315+omsherikar@users.noreply.github.com>
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Copilot AI commented Dec 17, 2025

@copilot Some CI are failing fix them

Fixed in commit 18f8459. The CI failures were due to code formatting issues:

  • Reformatted 9 files with Black
  • Fixed import sorting with isort

All 323 tests pass and all CI checks (Black, isort, flake8) now pass successfully.

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Pull request overview

Copilot reviewed 9 out of 9 changed files in this pull request and generated 13 comments.

Comments suppressed due to low confidence (1)

refactron/core/risk_assessment.py:7

  • Import of 'Set' is not used.
from typing import Dict, List, Optional, Set

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change_type: ChangeType,
affected_lines: Optional[List[int]] = None,
operation_description: str = "",
) -> tuple[float, RiskFactors]:
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The type hint uses lowercase tuple[float, RiskFactors] which requires Python 3.9+, but the project specifies requires-python = ">=3.8" in pyproject.toml. This will cause a TypeError on Python 3.8. Import Tuple from the typing module (which is already imported at line 7) and change the return type to Tuple[float, RiskFactors] for Python 3.8 compatibility.

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@copilot apply changes based on this feedback

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Fixed in commit 4c9de4b. Changed type hint from tuple[float, RiskFactors] to Tuple[float, RiskFactors] for Python 3.8 compatibility.

source_code: str,
change_type: ChangeType,
affected_lines: Optional[List[int]] = None,
operation_description: str = "",
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The operation_description parameter is defined but never used in the calculate_risk_score method. Either remove it if it's not needed, or incorporate it into the risk assessment logic (e.g., for logging or storing in RiskFactors metadata). Currently it adds unnecessary API complexity without providing value.

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refactron/cli.py Outdated
Comment on lines 154 to 168
if risk_score < 0.3:
risk_color = "green"
risk_label = "✓ SAFE"
elif risk_score < 0.5:
risk_color = "blue"
risk_label = "⚡ LOW RISK"
elif risk_score < 0.7:
risk_color = "yellow"
risk_label = "⚠ MODERATE RISK"
elif risk_score < 0.9:
risk_color = "red"
risk_label = "❌ HIGH RISK"
else:
risk_color = "bright_red"
risk_label = "🔴 CRITICAL RISK"
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The risk level thresholds (0.3, 0.5, 0.7, 0.9) and their corresponding labels are duplicated between _print_operation_with_risk in cli.py and _get_risk_icon in refactor_result.py. This creates maintenance burden and risk of inconsistency. Consider extracting a shared utility function or using the get_risk_level method from RiskAssessor class to determine these labels consistently across the codebase.

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@copilot apply changes based on this feedback

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Fixed in commit 3f2103e. Centralized risk display logic in RiskAssessor class:

  • Added RISK_DISPLAY_LABELS and RISK_DISPLAY_COLORS class constants
  • Created get_risk_display_label() and get_risk_display_color() helper methods
  • Updated both cli.py and refactor_result.py to use these centralized methods
  • Eliminated all duplicated threshold values (0.3, 0.5, 0.7, 0.9)

Comment on lines 266 to 273
elif total_deps < 30:
return 0.5
else:
return 0.7

except SyntaxError:
return 0.5 # Unknown, assume moderate risk

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The dependency risk thresholds (5, 15, 30) and return values (0.1, 0.3, 0.5, 0.7) are hardcoded magic numbers. Consider extracting these as class constants for better maintainability and to make it easier to tune the risk assessment in the future. For example: DEPENDENCY_THRESHOLD_LOW = 5, DEPENDENCY_THRESHOLD_MEDIUM = 15, DEPENDENCY_THRESHOLD_HIGH = 30.

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@copilot apply changes based on this feedback

omsherikar and others added 2 commits December 17, 2025 22:39
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
omsherikar and others added 3 commits December 17, 2025 22:41
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
…agic numbers, and populate test_file_exists

Co-authored-by: omsherikar <180152315+omsherikar@users.noreply.github.com>
…gic in RiskAssessor

Co-authored-by: omsherikar <180152315+omsherikar@users.noreply.github.com>
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Pull request overview

Copilot reviewed 9 out of 9 changed files in this pull request and generated 6 comments.


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Comment on lines +109 to +114
def _get_risk_icon(self, risk_score: float) -> str:
"""Get visual indicator for risk level."""
from refactron.core.risk_assessment import RiskAssessor

assessor = RiskAssessor()
return assessor.get_risk_display_label(risk_score)
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A new RiskAssessor instance is created every time _get_risk_icon is called, which happens for each operation in show_diff. Since show_diff can iterate over many operations (line 53), this could create many unnecessary RiskAssessor objects. Consider creating a single RiskAssessor instance at the class level or reusing one instance within the show_diff method.

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from refactron.core.risk_assessment import RiskAssessor

# Determine risk level and color using centralized logic
assessor = RiskAssessor()
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A new RiskAssessor instance is created in _print_operation_with_risk for every operation printed. If this function is called multiple times (e.g., in a loop for many operations), it creates unnecessary objects. Consider accepting a RiskAssessor instance as a parameter or creating it once in the calling code and reusing it.

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Comment on lines +148 to +183
def _print_operation_with_risk(operation) -> None:
"""Print refactoring operation with color-coded risk visualization."""
from rich.panel import Panel

from refactron.core.risk_assessment import RiskAssessor

# Determine risk level and color using centralized logic
assessor = RiskAssessor()
risk_score = operation.risk_score
risk_color = assessor.get_risk_display_color(risk_score)
risk_label = assessor.get_risk_display_label(risk_score)

# Build content
content = f"[bold]{operation.operation_type}[/bold]\n"
content += f"Location: {operation.file_path}:{operation.line_number}\n"
content += f"Risk: [{risk_color}]{risk_score:.2f} - {risk_label}[/{risk_color}]\n"
content += f"\n{operation.description}"

# Add risk factors if available
if "risk_factors" in operation.metadata:
risk_factors = operation.metadata["risk_factors"]
content += "\n\n[bold]Risk Breakdown:[/bold]\n"
content += f" • Impact Scope: {risk_factors.get('impact_scope', 0):.2f}\n"
content += f" • Change Type: {risk_factors.get('change_type_risk', 0):.2f}\n"
content += f" • Test Coverage: {risk_factors.get('test_coverage_risk', 0):.2f}\n"
content += f" • Dependencies: {risk_factors.get('dependency_risk', 0):.2f}\n"
content += f" • Complexity: {risk_factors.get('complexity_risk', 0):.2f}"

if risk_factors.get("test_file_exists"):
content += "\n\n✓ Test file exists"
else:
content += "\n\n⚠ No test file found - higher risk"

panel = Panel(content, border_style=risk_color, expand=False)
console.print(panel)

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The function _print_operation_with_risk is defined but never called anywhere in the codebase. The refactor command at line 309 uses result.show_diff() instead. Either this function should be integrated into the CLI workflow (replacing or complementing show_diff), or it should be removed if it's not needed. If it's intended for future use, consider adding a TODO comment explaining when and how it should be used.

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Comment on lines +448 to +451
for py_file in self.project_root.rglob("*.py"):
if py_file == file_path:
continue

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The for loop that begins on line 448 iterates through all Python files but doesn't do anything with them - it just continues immediately after the check on line 450. This loop appears to serve no purpose, as all the actual work happens in the second for loop starting at line 457. This first loop should be removed to eliminate dead code.

Suggested change
for py_file in self.project_root.rglob("*.py"):
if py_file == file_path:
continue

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# Find dependent test files
test_file = self._find_test_file(file_path)
if test_file:
impact["dependent_tests"].append(str(test_file))
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The test file lookup is called every time analyze_dependency_impact is invoked and may result in duplicate test files in the dependent_tests list. If analyze_dependency_impact is called multiple times for the same file, the same test file will be appended repeatedly. Consider checking if the test file is already in the list before appending, or ensuring this method is only called once per file.

Suggested change
impact["dependent_tests"].append(str(test_file))
test_file_str = str(test_file)
if test_file_str not in impact["dependent_tests"]:
impact["dependent_tests"].append(test_file_str)

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continue

# Limit the number of files scanned to avoid excessive I/O on large projects.
max_files = getattr(self, "max_dependency_scan_files", 1000)
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The max_files limit is retrieved using getattr with a default of 1000, but this value is not defined as a class constant. Consider extracting this magic number to a class constant like MAX_DEPENDENCY_SCAN_FILES = 1000 for consistency with other constants in the class and to make it easier to configure.

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2.1 Advanced Risk Assessment

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