Beyond Blame: Rethinking SZZ with Knowledge Graph Search

• URL: http://arxiv.org/abs/2602.02934v1
• Date: Tue, 03 Feb 2026 00:10:48 GMT
• Title: Beyond Blame: Rethinking SZZ with Knowledge Graph Search
• Authors: Yu Shi, Hao Li, Bram Adams, Ahmed E. Hassan,
• Abstract summary: We present AgenticSZZ, the first approach to apply Temporal Knowledge Graphs (TKGs) to software evolution analysis.<n>We show that AgenticSZZ achieves F1-scores of 0.48 to 0.74, with statistically significant improvements over state-of-the-art by up to 27%.
• Score: 13.82629698836299
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying Bug-Inducing Commits (BICs) is fundamental for understanding software defects and enabling downstream tasks such as defect prediction and automated program repair. Yet existing SZZ-based approaches are limited by their reliance on git blame, which restricts the search space to commits that directly modified the fixed lines. Our preliminary study on 2,102 validated bug-fixing commits reveals that this limitation is significant: over 40% of cases cannot be solved by blame alone, as 28% of BICs require traversing commit history beyond blame results and 14% are blameless. We present AgenticSZZ, the first approach to apply Temporal Knowledge Graphs (TKGs) to software evolution analysis. AgenticSZZ reframes BIC identification from a ranking problem over blame commits into a graph search problem, where temporal ordering is fundamental to causal reasoning about bug introduction. The approach operates in two phases: (1) constructing a TKG that encodes commits with temporal and structural relationships, expanding the search space by traversing file history backward from two reference points (blame commits and the BFC); and (2) leveraging an LLM agent to navigate the graph using specialized tools for candidate exploration and causal analysis. Evaluation on three datasets shows that AgenticSZZ achieves F1-scores of 0.48 to 0.74, with statistically significant improvements over state-of-the-art by up to 27%. Our ablation study confirms that both components are essential, reflecting a classic exploration-exploitation trade-off: the TKG expands the search space while the agent provides intelligent selection. By transforming BIC identification into a graph search problem, we open a new research direction for temporal and causal reasoning in software evolution analysis.

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