Case study of feature based awareness in a commercial software team and implications for the design of collaborative tools
Bibliographic record
Abstract
Software development is a process in continuous evolution. This characteristic implies also continuous changes in the functionality of the system under development. Some of these changes may cause problems when they are not properly and timely propagated to the project members. The aim of our research is to obtain a good understanding of problems caused by the lack of awareness of changes to features during a distributed software development project, to identify information and artifact repositories used by contributors, and then to draw the requirements of an awareness mechanism to tackle the awareness problem. In order to accomplish our research goals. we conducted a four month long case study at IBM Ottawa Software Lab. which we observed the collaboration patterns of a multi-site development project team. Our findings helped us identify the most important communication media that support development. In particular, we observed that the 3-1% of communication was by phone and via face-to-face interactions. and email was mostly used to alert contributors about changes to features. We also found that changes were not properly and timely propagated due to different corporate cultures of the project teams. Finally, we found that a high volume of communication makes developers prone to overlook important information that can lead to the generation of errors during development., These findings led us to believe that miscommunication and non-timely communication of changes related to feature development caused the release of code that created failures in stable builds. To address this problem. we developed the concept of a relationship to link developers to features. Using this concept, we have designed a feature-based Awareness Mechanism System to collect information, create relationships and deliver awareness information to the contributors involved in the implementation of a feature.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".