Towards Better Graph Neural Network-Based Fault Localization through Enhanced Code Representation
Bibliographic record
Abstract
Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization is a commonly used technique, which applies statistics on coverage spectra to rank faulty code based on suspiciousness scores. However, statisticsbased approaches based on formulae are often rigid, which calls for learning-based techniques. Amongst all, Grace , a graph-neural network (GNN) based technique has achieved state-of-the-art due to its capacity to preserve coverage spectra, i.e., test-to-source coverage relationships, as precise abstract syntax-enhanced graph representation, mitigating the limitation of other learning-based technique which compresses the feature representation. However, such representation is not scalable due to the increasing complexity of software, correlating with increasing coverage spectra and AST graph, making it challenging to extend, let alone train the graph neural network in practice. In this work, we proposed a new graph representation, DepGraph , that reduces the complexity of the graph representation by 70 % in nodes and edges by integrating the interprocedural call graph in the graph representation of the code. Moreover, we integrate additional features—code change information—into the graph as attributes so the model can leverage rich historical project data. We evaluate DepGraph using Defects4j 2.0.0, and it outperforms Grace by locating 20 % more faults in Top-1 and improving the Mean First Rank (MFR) and the Mean Average Rank (MAR) by over 50 % while decreasing GPU memory usage by 44 % and training/inference time by 85 % . Additionally, in cross-project settings, DepGraph surpasses the state-of-the-art baseline with a 42 % higher Top-1 accuracy, and 68 % and 65 % improvement in MFR and MAR, respectively. Our study demonstrates DepGraph ’s robustness, achieving state-of-the-art accuracy and scalability for future extension and adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".