Semantic Code Refactoring for Abstract Data Types
Bibliographic record
Abstract
Modifications to the data representation of an abstract data type (ADT) can require significant semantic refactoring of the code. Motivated by this observation, this paper presents a new method to automate semantic code refactoring tasks. Our method takes as input the original ADT implementation, a new data representation, and a so-called relational representation invariant (relating the old and new data representations), and automatically generates a new ADT implementation that is semantically equivalent to the original version. Our method is based on counterexample-guided inductive synthesis (CEGIS) but leverages three key ideas that allow it to handle real-world refactoring tasks. First, our approach reduces the underlying relational synthesis problem to a set of (simpler) programming-by-example problems, one for each method in the ADT. Second, it leverages symbolic reasoning techniques, based on logical abduction, to deduce code snippets that should occur in the refactored version. Finally, it utilizes a notion of partial equivalence to make inductive synthesis much more effective in this setting. We have implemented the proposed approach in a new tool called Revamp for automatically refactoring Java classes and evaluated it on 30 Java class mined from Github. Our evaluation shows that Revamp can correctly refactor the entire ADT in 97% of the cases and that it can successfully re-implement 144 out of the 146 methods that require modifications.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".