Assisting Bug Report Assignment Using Automated Fault Localisation: An Industrial Case Study
Bibliographic record
Abstract
We present a case study of an industry scale application of automated fault localisation to SAP HANA2 database. When a test breaks in the Continuous Integration (CI) pipeline, the bug needs to be triaged and assigned to the appropriate development team. Given the scale and complexity of SAP HANA2, the assignment itself can be a challenging task. The current practice depends on the static mapping between test scripts and software components, as well as human domain knowledge. We apply automated fault localisation to aid the issue allocation in the CI pipeline: once a test failure is observed, the automated fault localisation technique identifies the suspicious software component using the information from the test failure. The localisation result can be used by the issue manager to allocate the incoming test failure issues more efficiently. We have analysed 137 CI test executions with at least one failing test script using Spectrum Based Fault Localisation. The results show that automated fault localisation can identify the faulty software component for 61 out of 137 studied test failures within top 10 places out of over 200 components. Out of the 61 faults, 36 faults were not identifiable based on the static mapping between test script and software components at all.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".