Towards Diverse Program Transformations for Program Simplification
Bibliographic record
Abstract
By reducing the number of lines of code, program simplification reduces code complexity, improving software maintainability and code comprehension. While several existing techniques can be used for automatic program simplification, there is no consensus on the effectiveness of these approaches. We present the first study on how real-world developers simplify programs in open-source software projects. By analyzing 382 pull requests from 296 projects, we summarize the types of program transformations used, the motivations behind simplifications, and the set of program transformations that have not been covered by existing refactoring types. As a result of our study, we submitted eight bug reports to a widely used refactoring detection tool, RefactoringMiner, where seven were fixed. Our study also identifies gaps in applying existing approaches for automating program simplification and outlines the criteria for designing automatic program simplification techniques. In light of these observations, we propose SimpT5, a tool to automatically produce simplified programs that are semantically equivalent programs with reduced lines of code. SimpT5 is trained on our collected dataset of 92,485 simplified programs with two heuristics: (1) modified line localization that encodes lines changed in simplified programs, and (2) checkers that measure the quality of generated programs. Experimental results show that SimpT5 outperforms prior approaches in automating developer-induced program simplification.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".