BuildBot: Robotic Monitoring of Agile Software Development Teams
Bibliographic record
Abstract
In this paper, we describe BuildBot, a robotic interface developed to assist with the continuous integration process utilized by co-located agile software development teams. BuildBot's physical nature allows us to engage the agile software development team members through vision, hearing and touch. In this way, BuildBot becomes an active part of the development process by bringing together human-robot interaction, human group dynamics and software engineering concepts through a number of interaction modalities. In this paper we describe the design and implementation of the BuildBot prototype, a robotic interface that can sense virtual stimuli, in this case the state of a software build, and react accordingly in a physical way via vision, sound and touch. We present an early evaluation comparing BuildBot to two other tools used by an agile team to monitor the continuous integration process. We also show preliminary results indicating that BuildBot may be more noticeable to the developers and contribute to a fun and lighthearted atmosphere. We argue that by increasing awareness of the state of the software build, BuildBot can assist in the self-supervision of agile software engineering teams and can help the team achieve its goals in a more engaging and sociable manner.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".