T-Rec: Fine-Grained Language-Agnostic Program Reduction Guided by Lexical Syntax
Bibliographic record
Abstract
Program reduction strives to eliminate bug-irrelevant code elements from a bug-triggering program, so that (1) a smaller and more straightforward bug-triggering program can be obtained, (2) and the difference among duplicates (i.e., different programs that trigger the same bug) can be minimized or even eliminated. With such reduction and canonicalization functionality, program reduction facilitates debugging for software, especially language toolchains, such as compilers, interpreters, and debuggers. While many program reduction techniques have been proposed, most of them (especially the language-agnostic ones) overlooked the potential reduction opportunities hidden within tokens. Therefore, their capabilities in terms of reduction and canonicalization are significantly restricted. To fill this gap, we propose \(\mathsf{T}\) - \(\mathsf{Rec}\) , a fine-grained language-agnostic program reduction technique guided by lexical syntax. Instead of treating tokens as atomic and irreducible components, \(\mathsf{T}\) - \(\mathsf{Rec}\) introduces a fine-grained reduction process that leverages the lexical syntax of programming languages to effectively explore the reduction opportunities in tokens. Through comprehensive evaluations with versatile benchmark suites, we demonstrate that \(\mathsf{T}\) - \(\mathsf{Rec}\) significantly improves the reduction and canonicalization capability of two existing language-agnostic program reducers (i.e., Perses and Vulcan). \(\mathsf{T}\) - \(\mathsf{Rec}\) enables Perses and Vulcan to further eliminate 1,294 and 1,315 duplicates in a benchmark suite that contains 3,796 test cases that trigger 46 unique bugs. Additionally, \(\mathsf{T}\) - \(\mathsf{Rec}\) can also reduce up to 65.52% and 53.73% bytes in the results of Perses and Vulcan on our multi-lingual benchmark suite, respectively.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".