semantic-szz-analyzer

To identify bug-introducing commits with fewer false positives, apply semantic SZZ analysis which filters out cosmetic changes like renames and reformats using AST-diff meaningfulness and def-use chain relevance. This yields higher-confidence bug-introduction data than classic SZZ.

To improve classic SZZ precision when it flags too many unrelated commits, apply semantic code understanding to re-rank candidates based on semantic distance, temporal proximity, and author signal. This intelligent re-ranking isolates the most probable bug-introducing commit.

Semantic code understanding detects bug-introducing commits by moving beyond simple line-level blame to analyze AST diffs, def-use chains, and bug-pattern matching. It identifies semantic-preserving refactorings to exclude them, pinpointing the actual code change that caused the bug.

Yes, you can filter out semantic-preserving refactoring commits from SZZ bug analysis by applying semantic filtering. The analysis detects refactorings through AST-diff meaningfulness and bug-pattern matching to exclude them from the final bug-introduction candidate list.

You need semantic SZZ analysis for commit history debugging when classic SZZ precision is low or when high-confidence bug-introduction data is required. It is specifically designed for post-SZZ analysis to refine broad candidate lists into the most probable bug sources.

Classic SZZ identifies unrelated git commits as bug-introducers because it relies on simple line-level blame without understanding code semantics. You can refine the list by applying semantic filtering and intelligent re-ranking based on AST diffs and bug-pattern matching.