Facilitating Coordination between Software Developers: A Study and Techniques for Timely and Efficient Recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it