Assisting developers towards fault localization by analyzing failure reports
Bibliographic record
Abstract
Large software applications encompass many components with complex interdependencies. When a failure occurs, developers usually have limited information and time in their disposal for localizing the root cause of the observed failure. The most common information developers have readily access to includes failure reports, stack traces, and event logs. In this context, a major challenge is to devise techniques that assist developers utilize this information in order to zero-in their focus on specific methods that have a high probability of containing the root cause of the observed failure. Once such an initial set of methods has been identified, other more elaborate, complex, and computationally expensive data flow analyses could be applied. In this paper, we present a technique which aims to identify such an initial set of suspicious methods by first, retrieving information from failure reports obtained from Bugzilla repositories, second by combining this information with graph models that denote actual dependencies obtained from the subject system's source code in order to create an hypothesis space and third, by applying a ranking score to identify methods that have high likelihood of containing the root cause. The technique is shown to be tractable when applied to systems with several thousands of source code methods and the results exhibit high accuracy.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".