An empirical study of build system migrations in practice: Case studies on KDE and the Linux kernel
Bibliographic record
Abstract
As the build system, i.e. the infrastructure that constructs executable deliverables out of source code and other resources, tries to catch up with the ever-evolving source code base, its size and already significant complexity keep on growing. Recently, this has forced some major software projects to migrate their build systems towards more powerful build system technologies. Since at all times software developers, testers and QA personnel rely on a functional build system to do their job, a build system migration is a risky and possibly costly undertaking, yet no methodology, nor best practices have been devised for it. In order to understand the build system migration process, we empirically studied two failed and two successful attempts of build system migration in two major open source projects, i.e. Linux and KDE, by mining source code repositories and tens of thousands of developer mailing list messages. The major contributions of this paper are: (a) isolating the phases of a common methodology for build system migrations, which is similar to the spiral model for source code development (multiple iterations of a waterfall process); (b) identifying four of the major challenges associated with this methodology: requirements gathering, communication issues, performance vs. complexity of build system code, and effective evaluation of build system prototypes; (c) detailed analysis of the first challenge, i.e., requirements gathering for the new build system, which revealed that the failed migrations did not gather requirements rigorously. Based on our findings, practitioners will be able to make more informed decisions about migrating their build system, potentially saving them time and money.
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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 | 0.028 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".