GLAD: Neural Predicate Synthesis to Repair Omission Faults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Existing template and learning-based Automated Program Repair (APR) tools have successfully found patches for many benchmark faults. However, our analysis of existing results shows that omission faults pose a significant challenge. For template based approaches, omission faults provide no location to apply templates to; for learning based approaches that formulate repair as Neural Machine Translation (NMT), omission faults similarly do not provide faulty code to translate. To address these issues, we propose GLAD, a novel learning-based repair technique that targets if-clause synthesis. GLAD does not require a concrete faulty line as it is based on generative Language Models (LMs) instead of machine translation; consequently, it can repair omission faults. To provide the LM with project-specific information critical to synthesis, we incorporate two components: a type-based grammar that constrains the model, and a dynamic ranking system that evaluates candidate patches using a debugger. Our evaluation shows GLAD is highly orthogonal to existing techniques, correctly fixing 26 Defects4J v1.2 faults that previous NMT-based techniques could not, while maintaining a small runtime cost, underscoring its potential as a lightweight tool to complement existing tools in practice. An inspection of the bugs that GLAD fixes reveals that GLAD can quickly generate expressions that would be challenging for other techniques.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it