How tagging helps bridge the gap between social and technical aspects in software development
Bibliographic record
Abstract
Empirical research on collaborative software development practices indicates that technical and social aspects of software development are often intertwined. The processes followed are tacit and constantly evolving, thus not all of them are amenable to formal tool support. In this paper, we explore how ldquotaggingrdquo, a lightweight social computing mechanism, is used to bridge the gap between technical and social aspects of managing work items. We present the results from an empirical study on how tagging has been adopted and adapted over the past two years of a large project with 175 developers. Our research shows that the tagging mechanism was eagerly adopted by the team, and that it has become a significant part of many informal processes. Our findings indicate that lightweight informal tool support, prevalent in the social computing domain, may play an important role in improving team-based software development practices.
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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 | 0.035 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".