Semi-automating small-scale source code reuse via structural correspondence
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
Developers perform small-scale reuse tasks to save time and to increase the quality of their code, but due to their small scale, the costs of such tasks can quickly outweigh their benefits. Existing approaches focus on locating source code for reuse but do not support the integration of the located code within the developer's system, thereby leaving the developer with the burden of performing integration manually. This paper presents an approach that uses the developer's context to help integrate the reused source code into the developer's own source code. The approach approximates a theoretical framework (higher-order anti-unification modulo theories), known to be undecidable in general, to determine candidate correspondences between the source code to be reused and the developer's current (incomplete) system. This approach has been implemented in a prototype tool, called Jigsaw, that identifies and evaluates candidate correspondences greedily with respect to the highest similarity. Situations involving multiple candidate correspondences with similarities above a defined threshold are presented to the developer for resolution. Two empirical evaluations were conducted: an experiment comparing the quality of Jigsaw's results against suspected cases of small-scale reuse in an industrial system; and case studies with two industrial developers to consider its practical usefulness and usability issues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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