HistoRank: History-Based Ranking of Co-change Candidates
Bibliographic record
Abstract
Evolutionary coupling is a well investigated phenomenon during software evolution and maintenance. If two or more program entities co-change (i.e., change together) frequently during evolution, it is expected that the entities are coupled. This type of coupling is called evolutionary coupling or change coupling in the literature. Evolutionary coupling is realized using association rules and two measures: support and confidence. Association rules have been extensively used for predicting co-change candidates for a target program entity (i.e., an entity that a programmer attempts to change). However, association rules often predict a large number of co-change candidates with many false positives. Thus, it is important to rank the predicted co-change candidates so that the true positives get higher priorities. The predicted co-change candidates have always been ranked using the support and confidence measures of the association rules. In our research, we investigate five different ranking mechanisms on thousands of commits of ten diverse subject systems. On the basis of our findings, we propose a history-based ranking approach, HistoRank (History-based Ranking), that analyzes the previous ranking history to dynamically select the most appropriate one from those five ranking mechanisms for ranking co-change candidates of a target program entity. According to our experiment result, HistoRank outperforms each individual ranking mechanism with a significantly better MAP (mean average precision). We investigate different variants of HistoRank and realize that the variant that emphasizes the ranking in the most recent occurrence of co-change in the history performs the best.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".