An exploratory study on change suggestions for methods using clone detection
Bibliographic record
Abstract
A number of studies investigated providing change suggestions to programmers on the basis of the evolution history of a software system. While existing studies provide change suggestions considering code fragment level or even line level granularities, we investigate providing change suggestions at the method level. Providing a suggestion to change the entire method at one time is intuitively more time saving for developers compared to providing suggestions separately for different fragments of a method. In this research we empirically investigate whether we can infer change suggestions at the method level by analyzing the past evolution history of a software system through detection of method clones, and if so, then how we can rank the method level change suggestions. According to our investigation on thousands of commits of seven diverse subject systems, we can provide change suggestions at the method level with up to 83% precision and 13.49% recall. Moreover, for up to 34% of the commits we can provide correct method level change suggestions. Compared to the existing fragment level change suggestion techniques, our method level change suggestion technique has promising precision and recall. We investigate the ranking of method level change suggestions and find that recency ranking (i.e., ranking on the basis of how recently the change suggestions appeared in the past) is a better choice than frequency ranking (ranking considering how frequently the suggestions appeared). We believe that while a method level change suggestion technique can never be a replacement for the existing fine grained change suggestion techniques, it can complement these existing ones.
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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 | 0.023 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".