Why Do Automated Builds Break? An Empirical Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it