Taxonomy for software teamwork measurement
Bibliographic record
Abstract
ABSTRACT Despite the fact that software is mostly a team endeavor, the software engineering (SE) literature has not tapped into organizational psychology's conceptual and empirical writings on teams. This paper presents a model of team dynamics adapted to the specificities of SE project teams. The taxonomy is composed of nine episodes that are likely to be found in any software team process. Each episode is described in terms of the input‐process‐output cycle and illustrated with examples. The measurability of the episodes is validated on a capstone student project carried out with an industrial partner. The team activities are recorded by each developer, throughout the project's duration, in the form of work tokens. These work tokens are then associated with episodes by two independent coders. The results show that all the episodes of the proposed taxonomy are measurable, and very few (less than 5% in this field study) remain ambiguous. Most of the ambiguities arise from short episodes that alternate during team process activities. This paper's contribution to software team process research is to synthesize the team literature and draw up a theoretically driven taxonomy of team dynamics specific to SE teams and to provide initial evidence of measurability of the taxonomy. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.018 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".