Build Optimization: A Systematic Literature Review
Bibliographic record
Abstract
In modern software organizations, Continuous Integration (CI) consists of an automated build process triggered by change submissions and involving compilation, testing, and packaging to enable the continuous deployment of new software versions to end-users. While CI offers various advantages regarding software quality and delivery speed, it introduces challenges addressed by a large body of research. To better understand this literature, so as to help practitioners find solutions for their problems and guide future research, we conduct a systematic review of 97 studies published between 2006 and 2024, summarizing their goals, methodologies, datasets, and metrics. These studies target two main challenges: (1) long build durations and (2) build failures. To address the first, researchers have proposed techniques such as predicting build outcomes and durations, selective build execution, and build acceleration through caching or performance smell repair. On the other hand, build failure root causes have been studied, leading to techniques for predicting build script maintenance needs and automating repairs. Recent work also focuses on flaky build failures caused by environmental issues. Most techniques use machine learning and rely on build metrics, which we classify into five categories. Finally, we identify eight publicly available datasets to support future research on build optimization.
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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".