Detection of software evolution phases based on development activities
Bibliographic record
Abstract
Software evolution history is usually represented at fine granularity by commits in software repositories, and at coarse granularity by software releases. In order to gain insights on development activities and on software evolution, the information on releases is too general, whereas the information on commits is prohibitively large to be efficiently processed by a developer. This paper proposes an automatic technique for the identification of distinct phases of evolution. Such software evolution phases are characterized by similar development activities in terms of changes to entities. Therefore, our technique decomposes software evolution history to assist developers identify periods of different development activities. Our analysis technique is a search-based optimization of the best decomposition of commits from the software repository using heuristics such as classes changed in each commit, and the magnitude/importance of these changes. To validate our technique, we applied it on the evolution history of five case studies covering multiple releases over several years of development. An interesting outcome of the evaluation is that our automatic decomposition of software evolution history recovered the original decomposition in software releases.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".