Studying the Impact of Developer Communication on the Quality and Evolution of a Software System: A Doctoral Dissertation Retrospective
Bibliographic record
Abstract
Software development is a largely collaborative effort, of which the actual encoding of program logic in source code is a relatively small part. Software developers have to collaborate effectively and communicate with their peers in order to avoid coordination problems. To date, little is known how developer communication during software development activities impacts the quality and evolution of a software. In the doctoral thesis presented to this symposium, we introduce and evaluate tools and techniques to recover communication data from traces of the software development activities that are recorded in software repositories. Using this data, we present a study on the impact of developer communication on the quality and evolution of the software through a tiered study. Among the highlights of the presented dissertation, we find that communication between developers stands in a direct relationship to the quality of the software. In addition, we find that communication between developers plays a important role in maintaining a healthy contribution management process, which is one of the key factors to the successful evolution of the software. Our dissertation illustrates that software development is an intricate and complex process, which is strongly influenced by the social interactions between the stakeholders involved in the development activities. A traditional view based solely on technical aspects of software development such as source code size and complexity, while valuable, limits our understanding of software development activities. The presented research consists of a first step towards gaining a more holistic view on software development activities.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".