The dispersion of build maintenance activity across maven lifecycle phases
Bibliographic record
Abstract
Build systems describe how source code is translated into deliverables. Developers use build management tools like Maven to specify their build systems. Past work has shown that while Maven provides invaluable features (e.g., incremental building), it introduces an overhead on software development. Indeed, Maven build systems require maintenance. However, Maven build systems follow the build lifecycle, which is comprised of validate, compile, test, packaging, install, and deploy phases. Little is known about how build maintenance activity is dispersed among these lifecycle phases. To bridge this gap, in this paper, we analyze the dispersion of build maintenance activity across build lifecycle phases. Through analysis of 1,181 GitHub repositories that use Maven, we find that: (1) the compile phase accounts for 24% more of the build maintenance activity than the other phases; and (2) while the compile phase generates a consistent amount of maintenance activity over time, the other phases tend to generate peaks and valleys of maintenance activity. Software teams that use Maven should plan for these shifts in the characteristics of build maintenance activity.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".