Semantic Szz Analyzer Agent Skill

Semantic SZZ Analyzer

Overview

Semantic SZZ Analyzer extends the traditional SZZ (Sliwerski-Zimmermann-Zeller) algorithm by incorporating semantic analysis to identify bug-introducing commits more accurately. It distinguishes actual semantic changes from refactorings or code movements by analyzing control-flow and data-flow similarity across versions.

Core Capabilities

1. Semantic Change Detection

Analyze commits to distinguish between:

• Semantic changes: Modifications that alter program behavior
• Refactorings: Code restructuring without behavior changes
• Code movements: Relocations of code blocks without semantic impact

Use control-flow graphs (CFG) and data-flow analysis to compute similarity between code versions.

2. Bug-Introducing Commit Identification

Given a bug-fix commit, trace back through git history to identify the commit that introduced the bug:

• Extract changed lines from the bug-fix commit
• Use git blame to find commits that last modified those lines
• Apply semantic analysis to filter out false positives
• Rank candidates by semantic similarity and temporal proximity

3. False Positive Reduction

Traditional SZZ produces many false positives due to:

• Whitespace changes
• Comment modifications
• Import reorganization
• Variable renaming
• Code formatting

Semantic SZZ filters these by analyzing AST (Abstract Syntax Tree) structure and semantic equivalence.

Workflow

Step 1: Analyze Bug-Fix Commit

Start by identifying the bug-fix commit. Look for:

• Commits with keywords: "fix", "bug", "issue", "patch", "resolve"
• Commits linked to issue trackers
• Commits explicitly marked as fixes

Extract the changed lines and affected files.

Step 2: Identify Candidate Commits

Use git blame or git log -L to trace the history of changed lines:

git blame -L <start>,<end> <file> <bug-fix-commit>^

This identifies commits that last modified the buggy lines before the fix.

Step 3: Apply Semantic Analysis

For each candidate commit, run semantic analysis using the provided script:

python scripts/semantic_analyzer.py --repo <repo-path> --candidate <commit-hash> --fix <fix-commit-hash>

The script computes:

• CFG similarity: Control-flow graph matching between versions
• Data-flow similarity: Variable usage and dependency analysis
• AST diff: Structural code changes vs. superficial changes

Step 4: Filter and Rank Results

Filter candidates based on semantic similarity threshold (default: 0.7). Rank remaining candidates by:

• Semantic change magnitude
• Temporal proximity to bug-fix
• Code churn in the commit

Step 5: Generate Explanation

For each identified bug-introducing commit, generate an explanation including:

• What semantic changes were made
• Why the change is considered bug-introducing
• Confidence score based on similarity metrics
• Diff highlighting the problematic changes

Usage Examples

Example 1: Analyze a specific bug-fix

python scripts/semantic_szz.py --repo /path/to/repo --fix-commit abc123

Example 2: Batch analysis of multiple fixes

python scripts/batch_analyze.py --repo /path/to/repo --fixes-file bug_fixes.txt

Example 3: Generate detailed report

python scripts/semantic_szz.py --repo /path/to/repo --fix-commit abc123 --output report.json --explain

Advanced Features

Custom Similarity Thresholds

Adjust sensitivity by modifying similarity thresholds:

# In scripts/semantic_analyzer.py
CFG_THRESHOLD = 0.7  # Control-flow similarity
DFG_THRESHOLD = 0.6  # Data-flow similarity
AST_THRESHOLD = 0.8  # AST structural similarity

Language-Specific Analysis

The analyzer supports multiple languages with language-specific parsers:

• Python: Uses ast module
• Java: Uses javalang or tree-sitter
• C/C++: Uses pycparser or tree-sitter
• JavaScript: Uses esprima or tree-sitter

See references/language_support.md for details.

Integration with Issue Trackers

Link bug-fixes to issue IDs for automated analysis:

python scripts/semantic_szz.py --repo /path/to/repo --issue JIRA-123

References

• references/szz_algorithm.md: Detailed explanation of traditional SZZ algorithm
• references/semantic_analysis.md: Control-flow and data-flow analysis techniques
• references/language_support.md: Language-specific parsing and analysis details

Output Format

Results are provided in JSON format:

{
  "fix_commit": "abc123",
  "bug_introducing_commits": [
    {
      "commit": "def456",
      "confidence": 0.85,
      "semantic_change_type": "logic_modification",
      "explanation": "Modified conditional logic in function foo()",
      "changed_lines": [45, 46, 47],
      "similarity_scores": {
        "cfg": 0.72,
        "dfg": 0.68,
        "ast": 0.81
      }
    }
  ]
}