LPR: Large Language Models-Aided Program Reduction
Bibliographic record
Abstract
Program reduction is a prevalent technique to facilitate compilers' debugging by automatically minimizing bug-triggering programs. Existing program reduction techniques are either generic across languages (e.g., Perses and Vulcan) or specifically customized for one certain language by employing language-specific features, like C-Reduce. However, striking the balance between generality across multiple programming languages and specificity to individual languages in program reduction is yet to be explored. This paper proposes LPR, the first technique utilizing LLMs to perform language-specific program reduction for multiple languages. The core insight is to utilize both the language-generic syntax level program reduction (e.g., Perses) and the language-specific semantic level program transformations learned by LLMs. Alternately, language-generic program reducers efficiently reduce programs into 1-tree-minimality, which is small enough to be manageable for LLMs; LLMs effectively transform programs via the learned semantics to expose new reduction opportunities for the language-generic program reducers to further reduce the programs. Our extensive evaluation on 50 benchmarks across three languages (C, Rust, and JavaScript) has highlighted LPR's practicality and superiority over Vulcan, the state-of-the-art language-generic program reducer. For effectiveness, LPR surpasses Vulcan by producing 24.93%, 4.47%, and 11.71% smaller programs on benchmarks in C, Rust and JavaScript. Moreover, LPR and Vulcan have demonstrated their potential to complement each other. By using Vulcan on LPR's output for C programs, we achieve program sizes comparable to those reduced by C-Reduce. For efficiency, LPR takes 10.77%, 34.88%, 36.96% less time than Vulcan to finish all benchmarks in C, Rust and JavaScript, separately.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".