A Network Analysis of Stakeholders in Tool Visioning Process for Story Test Driven Development
Bibliographic record
Abstract
Participation from all stakeholders is important in a successful software development project, especially if the development project is complex and has many stakeholders. Identifying the key stakeholders is very difficult in a large community-based open source development project, because a lot of conflicting ideas exist in the community and not all of the necessary stakeholders are represented in the discussions. We analyzed the homogeneity of the stakeholders in the story-test driven development tool community and the diversity of the opinions represented by the stakeholders. We gathered opinions from the agile software engineering community on a list of desired features in a story testing tool. Then we categorize the community using a social network analysis to analyze the consensus building process. The network analysis reveals that the community has several key people with dominant degree centrality in the social network and the tool development community is remarkably homogeneous. Our research shows that a social network analysis is a good way to analyze the characteristics of consensus reached during a product visioning process.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".