Making the Case for LLM-Generated Automated Program Repair Benchmarks
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
Automated Program Repair (APR) has made significant strides in recent years, particularly with the integration of large language models (LLMs) and deep learning techniques. Yet despite this progress, one fundamental issue continues to hinder advancement: how we evaluate these systems. Many of today’s APR benchmarks suffer from serious limitations—including small dataset sizes, synthetic or unrealistic bug scenarios, overfitting risks, ambiguous evaluation criteria, and a narrow focus on certain programming languages.In this paper, we take a critical look at these challenges by identifying eight core limitations in widely used benchmarks. We then explore how LLM-generated benchmarks can help overcome these obstacles. Finally, we address some potential concerns about LLM-generated benchmarks and propose a quality assurance and validation framework.By combining the strengths of LLMs with thoughtful benchmark design, this work lays the foundation for more robust, diverse, and meaningful evaluation frameworks—paving the way for future breakthroughs in APR research.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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