How Developers' Collaborations Identified from Different Sources Tell Us about Code Changes
Bibliographic record
Abstract
Written communications recorded through channels such as mailing lists or issue trackers, but also code co-changes, have been used to identify emerging collaborations in software projects. Also, such data has been used to identify the relation between developers' roles in communication networks and source code changes, or to identify mentors aiding newcomers to evolve the software project. However, results of such analyses may be different depending on the communication channel being mined. This paper investigates how collaboration links vary and complement each other when they are identified through data from three different kinds of communication channels, i.e., mailing lists, issue trackers, and IRC chat logs. Also, the study investigates how such links overlap with links mined from code changes, and how the use of different sources would influence (i) the identification of project mentors, and (ii) the presence of a correlation between the social role of a developer and her changes. Results of a study conducted on seven open source projects indicate that the overlap of communication links between the various sources is relatively low, and that the application of networks obtained from different sources may lead to different results.
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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 | 0.005 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| 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".