The Impact of Operating Systems and Environments on Build Results
Bibliographic record
Abstract
Continuous Integration (CI) is a software engineering practice to identify and correct a defect as soon as possible after a code change has been integrated into the version control system.The main purpose of CI is to give developers a quick feedback of code changes.These changes build on different OSes and runtime environments to check backward compatibility as well as to check if the product still works with the new changes.So, many builds are performed, while only a few of them can identify new failures.In other words, a phenomenon of build inflation can be observed, where the increasing number of builds has diminishing returns in terms of identified failures vs. costs of running the builds.This inflation makes interpreting build results challenging as it increases the importance of some failures, while it hides the importance of others.This thesis advances our understanding of the impact of OSes and runtime environments on build failures and build inflation through a large-scale study of 30 million builds of the CPAN ecosystem.We choose CPAN because CPAN provides a rich data set for the analysis of automated builds on dozens of environments (Perl versions) and operating systems.This thesis performs quantitative and qualitative analysis on build failures to classify these failures and find out the reason of their occurrence.We observe: (1) the evolution of build failures over time and report that while more builds are being performed, the percentage of them identifying a failure drops, (2) different OSes and environments are not equally reliable, (3) the build results of CI must be filtered to identify reliable failing data, (4) and most build failures are due to API dependency.Researchers and practitioners should consider the impact of build inflation when they are analyzing and-or performing builds.
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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 | 0.018 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".