Why Do Automated Builds Break? An Empirical Study
Bibliographic record
Abstract
To detect integration errors as quickly as possible, organizations use automated build systems. Such systems ensure that (1) the developers are able to integrate their parts into an executable whole, (2) the testers are able to test the built system, (3) and the release engineers are able to leverage the generated build to produce the upcoming release. The flipside of automated builds is that any incorrect change can break the build, and hence testing and releasing, and (even worse) block other developers from continuing their work, delaying the project even further. To measure the impact of such build breakage, this empirical study analyzes 3,214 builds produced in a large software company over a period of 6 months. We found a high ratio of build breakage (17.9%), and also quantified the cost of such build breakage as more than 336.18 man-hours. Interviews with 28 software engineers from the company helped to understand the circumstances under which builds are broken and the effects of build breakages on the collaboration and coordination of teams. We quantitatively investigated the main factors impacting build breakage and found that build failures correlate with the number of simultaneous contributors on branches, the type of work items performed on a branch, and the roles played by the stakeholders of the builds (for example developers vs. Integrators).
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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 | 0.015 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".