An empirical study of build maintenance effort
Why this work is in the frame
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Bibliographic record
Abstract
The build system of a software project is responsible for transforming source code and other development artifacts into executable programs and deliverables. Similar to source code, build system specifications require maintenance to cope with newly implemented features, changes to imported Application Program Interfaces (APIs), and source code restructuring. In this paper, we mine the version histories of one proprietary and nine open source projects of different sizes and domain to analyze the overhead that build maintenance imposes on developers. We split our analysis into two dimensions: (1) Build Coupling, i.e., how frequently source code changes require build changes, and (2) Build Ownership, i.e., the proportion of developers responsible for build maintenance. Our results indicate that, despite the difference in scale, the build system churn rate is comparable to that of the source code, and build changes induce more relative churn on the build system than source code changes induce on the source code. Furthermore, build maintenance yields up to a 27% overhead on source code development and a 44% overhead on test development. Up to 79% of source code developers and 89% of test code developers are significantly impacted by build maintenance, yet investment in build experts can reduce the proportion of impacted developers to 22% of source code developers and 24% of test code developers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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