Time Travel: LLM-Assisted Semantic Behavior Localization with Git Bisect
Bibliographic record
Abstract
We present a novel framework that integrates Large Language Models (LLMs) into the Git bisect process for semantic fault localization. Traditional bisect assumes deterministic predicates and binary failure states—assumptions often violated in modern software development due to flaky tests, non- monotonic regressions, and semantic divergence from upstream repositories. Our system augments bisect traversal with structured chain-of-thought reasoning, enabling commit-by-commit analysis under noisy conditions. We evaluate multiple open-source and proprietary LLMs for their suitability, and fine-tune DeepSeek- Coder-V2 using QLoRA on a curated dataset of semantically labeled diffs. We adopt a weak-supervision workflow to reduce annotation overhead, incorporating human-in-the-loop corrections and self-consistency filtering. Experiments across multiple open- source projects show a 6.4-point absolute gain in success rate (from 74.2% to 80.6%), leading to significantly fewer failed traversals and—by experiment—up to 2x reduction in average bisect time. We conclude with discussions on temporal reasoning, prompt design, and fine-tuning strategies tailored for commit-level behavior analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".