Facilitating Coordination between Software Developers: A Study and Techniques for Timely and Efficient Recommendations
Bibliographic record
Abstract
When software developers fail to coordinate, build failures, duplication of work, schedule slips and software defects can result. However, developers are often unaware of when they need to coordinate, and existing methods and tools that help make developers aware of their coordination needs do not provide timely or efficient recommendations. We describe our techniques to identify timely and efficient coordination recommendations, which we developed and evaluated in a study of coordination needs in the Mylyn software project. We describe how data obtained from tools that capture developer actions within their Integrated Development Environment (IDE) as they occur can be used to timely identify coordination needs; we also describe how properties of tasks coupled with machine learning can focus coordination recommendations to those that are more critical to the developers to reduce information overload and provide more efficient recommendations. We motivate our techniques through developer interviews and report on our quantitative analysis of coordination needs in the Mylyn project. Our results suggest that by leveraging IDE logging facilities, properties of tasks and machine learning techniques awareness tools could make developers aware of critical coordination needs in a timely way. We conclude by discussing implications for software engineering research and tool design.
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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 | 0.021 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".