Communication, collaboration, and bugs
Bibliographic record
Abstract
Issue tracking systems help organizations manage issue reporting, assignment, tracking, resolution, and archiving. Traditionally, it is the Software Engineering community that researches issue tracking systems, where software defects are reported and tracked as 'bug reports' within an archival database. Yet, as issue tracking is fundamentally a social process, it is important to understand the design and use of issue tracking systems from that perspective. Consequently, we conducted a qualitative study of issue tracking systems as used by small, collocated software development teams. We found that an issue tracker is not just a database for tracking bugs, features, and inquiries, but also a focal point for communication and coordination for many stakeholders within and beyond the software team. Customers, project managers, quality assurance personnel, and programmers all contribute to the shared knowledge and persistent communication that exists within the issue tracking system. These results were all the more striking because in spite of teams being collocated--which afforded frequent, face-to-face communication--the issue tracker was still used as a fundamental communication channel. We articulate various real-world practices surrounding issue trackers and offer design considerations for future systems.
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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".