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
      }
    }
  ]
}

semantic-szz-analyzer