Bibliographic record
Abstract
The build system, i.e., the infrastructure that converts source code into deliverables, plays a critical role in the development of a software project. For example, developers rely upon the build system to test and run their source code changes. Without a working build system, development progress grinds to a halt, as the source code is rendered useless. Based on experiences reported by developers, we conjecture that build maintenance for large software systems is considerable, yet this maintenance is not well understood. A firm understanding of build maintenance is essential for project managers to allocate personnel and resources to build maintenance tasks effectively, and reduce the build maintenance overhead on regular development tasks, such as fixing defects and adding new features. In our work, we empirically study build maintenance in one proprietary and nine open source projects of different sizes and domain. Our case studies thus far show that: (1) similar to Lehman's first law of software evolution, build system specifications tend to grow unless effort is invested into restructuring them, (2) the build system accounts for up to 31% of the code files in a project, and (3) up to 27% of development tasks that change the source code also require build maintenance. Currently, we are working on identifying concrete measures that projects can take to reduce the build maintenance overhead.
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.
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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".