How Useful is Code Change Information for Fault Localization in Continuous Integration?
Bibliographic record
Abstract
Continuous integration (CI) is the process in which code changes are automatically integrated, built, and tested in a shared repository. In CI, developers frequently merge and test code under development, which helps isolate faults with finer-grained change information. To identify faulty code, prior research has widely studied and evaluated the performance of spectrum-based fault localization (SBFL) techniques. While the continuous nature of CI requires the code changes to be atomic and presents fine-grained information on what part of the system is being changed, traditional SBFL techniques do not benefit from it. To overcome the limitation, we propose to integrate the code and coverage change information in fault localization under CI settings. First, code changes show how faults are introduced into the system, and provide developers with better understanding on the root cause. Second, coverage changes show how the code coverage is impacted when faults are introduced. This change information can help limit the search space of code coverage, which offers more opportunities for improving fault localization techniques. Based on the above observations, we propose three new change-based fault localization techniques, and compare them with Ochiai, a commonly used SBFL technique. We evaluate these techniques on 192 real faults from seven software systems. Our results show that all three change-based techniques outperform Ochiai on the Defects4J dataset. In particular, the improvement varies from 7% to 23% and 17% to 24% for average MAP and MRR, respectively. Moreover, we find that our change-based fault localization techniques can be integrated with Ochiai, and boost its performance by up to 53% and 52% for average MAP and MRR, respectively.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".