A catalog of ten cache smells in GitLab CI/CD, an automated detector achieving 0.98 F1, and empirical evidence that the smells appear in 89% of 228 mature open-source projects.
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2026 4roles
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Large-scale analysis of AI bot PRs shows Copilot and Codex achieve the highest CI/CD success rates but more frequent AI contributions correlate with reduced workflow reliability.
FlaXifyer applies few-shot learning on pre-trained language models to categorize intermittent CI job failures from logs at 84.3% Macro F1 and 92.0% Top-2 accuracy using 12 examples per category, with LogSift reducing log review effort by 74.4%.
An AI-enabled framework is proposed to assess CI suitability, recommend services, and guide configurations according to project characteristics.
citing papers explorer
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Cache-Related Smells in GitLab CI/CD: Comprehensive Catalog, Automated Detection, and Empirical Evidence
A catalog of ten cache smells in GitLab CI/CD, an automated detector achieving 0.98 F1, and empirical evidence that the smells appear in 89% of 228 mature open-source projects.
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Reliability of AI Bots Footprints in GitHub Actions CI/CD Workflows
Large-scale analysis of AI bot PRs shows Copilot and Codex achieve the highest CI/CD success rates but more frequent AI contributions correlate with reduced workflow reliability.
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Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models
FlaXifyer applies few-shot learning on pre-trained language models to categorize intermittent CI job failures from logs at 84.3% Macro F1 and 92.0% Top-2 accuracy using 12 examples per category, with LogSift reducing log review effort by 74.4%.
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A Vision for Context-Aware CI Adoption Decisions
An AI-enabled framework is proposed to assess CI suitability, recommend services, and guide configurations according to project characteristics.