Collaboration Patterns and the Impact of Distance on Awareness in Requirements-Centred Social Networks
Bibliographic record
Abstract
Because of intense collaborative needs, requirements engineering is a challenge in global software development. How do distributed teams manage the development of requirements in environments that require significant cross-site collaboration and coordination? In this paper, we report research that used social network analysis to explore collaboration and awareness among team members during requirements management in an industrial distributed software team. Using the lens of a requirements-centred social network to group team members who work on a particular requirement, we collected data to characterize requirements-centric collaborations in a project, and to examine aspects of awareness of requirements changes within these networks. Our findings indicate organic patterns of collaboration involving considerable cross-site interaction, in which communication of changes was the most predominant reason for interaction. Although we did not find evidence that distance affects developers' awareness of remote team members who work on the same requirements, distance affected how accessible the remote colleagues were. We discuss implications for knowledge sharing and coordination of work on a requirement in distributed teams, and propose directions for the design of collaboration tools that support awareness in distributed requirements management.
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".