A Call Graph Mining and Matching Based Defect Localization Technique
Bibliographic record
Abstract
Locating defects in the source code of a software system is one of the most challenging tasks in software debugging. Defect localization tools aim to assist developers in finding the location of defects. Both static and dynamic analysis approaches are used. In the case of dynamic approaches, two different scenarios apply. The first is one in which we have multiple (different) executions that exhibit the faulty behavior. The second is one in which we have just a single faulty execution. This is the focus of this paper. In this paper, we present a novel technique for localization of structure-affecting defects (i.e., defects that make an execution diverge from the expected path, thus creating unexpected dynamic call graphs), using tree mining and tree matching techniques. Given a target system and a failing test case, the proposed technique finds methods in the source code of the system which are likely to have caused the failure (i.e., defective) or lie on the same path in the dynamic call tree representation of the failing execution as the defective methods, and thus can be used as a starting point to find the defective method(s). The proposed defect localization technique is implemented as a prototype and evaluated using four subject programs of various sizes, developed in Java or C. Our experiments show comparable results to similar defect localization tools, but unlike most of its counterparts, our technique does not require the availability of multiple failing executions to localize the defects. We believe that this is a major advantage, since it is often the case that we have only a single failing execution to work with.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".