Using GitHub Analytics to Assess the Quality of Collaboration in Software Engineering Teams
Bibliographic record
Abstract
This research-to-practice full paper investigates using team process analytics from GitHub to support team management. Effective teamwork is essential in higher education learning and workplace success. The role of educators in supporting proper team functioning includes helping students learn how to participate actively and communicate effectively in meetings, delegate work fairly, manage high-quality work throughput, and resolve conflicts if problems arise. Problems often emerge when team members have differing visions or individuals do not contribute equally to the work output. These problems are exacerbated in large classes involving many teams. Detecting these potential issues in teams and helping students work through them is important for team success. In software engineering projects, monitoring individual contributions can begin with mining activities on programming platforms such as GitHub, which makes much of the individual contributions more visible and quantifiable. In this work, we propose a framework for fairly assessing teamwork and present the development of team analytics to assist educators in detecting potential issues in team collaboration. We describe a pilot study involving this tool in the context of a software engineering capstone course with 104 students split into 22 teams managed by 4 teaching assistants. Our results show that the reports offer value in guiding the evaluation process and identifying where problems may be, but do not replace the actual repository analysis where needed. We discuss the potential value of using this tool to improve collaboration.
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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 | 0.001 | 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 | 0.000 | 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".