Reverse engineering the process of small novice software teams
Bibliographic record
Abstract
The software-development project success dependson the technical competence of the development team,the quality of its tools and the project-managementdecisions it makes during the software lifecycle. Newrequirements, tight delivery schedules and team-memberturnaround present the team with challenges.Flexible decision making for effective adaptation tothese challenges is an extremely difficult skill toacquire, and even more challenging to teach.Instructors of software-engineering courses involvingcollaborative project development are oftenoverwhelmed by the task of monitoring the progress ofmultiple teams and problems in the team's process maygo unnoticed until it is too late to be fixed.In this paper we describe our work on analyzingthe CVS history of a team project repository to extractinformation about the nature of the collaborationbetween the members of a team. This analysis cansupport the instructor in noticing evidence of potentialproblems who can then use this information to alert theteam. It can also be shown to the team membersthemselves, so that they become more aware of theirprocess. We evaluate our CVS analysis process with acase study, based on an undergraduate software-engineeringcourse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".