Graph neural networks for precise bug localization through structural program analysis
Bibliographic record
Abstract
Abstract Bug localization (BL) is known as one of the major steps in the program repair process, which generally seeks to find a set of commands causing a program to crash or fail. At the present time, locating bugs and their sources quickly seems to be impossible as the complexity of modern software development and scaling is soaring. Accordingly, there is a huge demand for BL techniques with minimal human intervention. A graph representing source code typically encodes valuable information about both the syntactic and semantic structures of programs. Many software bugs are associated with these structures, making graphs particularly suitable for bug localization (BL). Therefore, the key contributions of this work involve labeling graph nodes, classifying these nodes, and addressing imbalanced classifications within the graph data structure to effectively locate bugs in code. A graph-based bug classifier is initially introduced in the method proposed in this paper. For this purpose, the program source codes are mapped to a graph representation. Since the graph nodes do not have labels, the Gumtree algorithm is then exploited to label them by comparing the buggy graphs and the corresponding bug-free ones. Afterward, a trained, supervised node classifier, developed based on a graph neural network (GNN), is applied to classify the nodes into buggy or bug-free ones. Given the imbalance in the data, accuracy, precision, recall, and F1-score metrics are used for evaluation. Experimental results on identical datasets show that the proposed method outperforms other related approaches. The proposed approach effectively localizes a broader spectrum of bug types, such as undefined properties, functional bugs, variable naming errors, and variable misuse issues .
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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".