Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions
Bibliographic record
Abstract
Build systems automate the integration of source code into executables. Maintaining build systems is known to be challenging. Lax build maintenance can lead to costly build breakages or unexpected software behaviour. Code review is a broadly adopted practice to improve software quality. Yet, little is known about how code review is applied to build specifications. In this paper, we present the first empirical study of how code review is practiced in the context of build specifications. Through quantitative analysis of 502,931 change sets from the Qt and Eclipse communities, we observe that changes to build specifications are at least two times less frequently discussed during code review when compared to production and test code changes. A qualitative analysis of 500 change sets reveals that (i) comments on changes to build specifications are more likely to point out defects than rates reported in the literature for production and test code, and (ii) evolvability and dependency-related issues are the most frequently raised patterns of issues. Follow-up interviews with nine developers with 1–40 years of experience point out social and technical factors that hinder rigorous review of build specifications, such as a prevailing lack of understanding of and interest in build systems among developers, and the lack of dedicated tooling to support the code review of build specifications.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".