OPTIMIZING CI/CD PIPELINES WITH AI-DRIVEN BUILD FAILURE PREDICTION: AN EMPIRICAL STUDY ON MACHINE LEARNING MODELS FOR EARLY FAILURE DETECTION

Abstract

Background:
Continuous Integration and Continuous Deployment (CI/CD) pipelines are central to modern DevOps practices, yet frequent build failures lead to wasted resources, delayed releases, and reduced developer productivity. Artificial intelligence (AI) offers a solution by predicting failures early, enabling proactive intervention and more efficient pipelines.

Objective:
This study evaluates the effectiveness of machine learning models in predicting build failures within CI/CD pipelines, with a focus on optimizing deployment speed, reducing wasted build cycles, and improving early failure detection.

Methods:
A dataset of 100,000 build records was collected from open-source projects using Jenkins, GitHub Actions, and GitLab CI (2020–2024). Features included commit metadata, test results, and pipeline performance metrics. Four models were trained—Logistic Regression, Random Forest, XGBoost, and Neural Networks. Model evaluation considered accuracy, precision, recall, F1-score, ROC-AUC, and Early Warning Lead Time (EWT). Statistical analysis employed ANOVA, Chi-square tests, and Cohen’s kappa.

Results:
XGBoost achieved the best performance (accuracy: 89.7%, F1-score: 0.89, ROC-AUC: 0.94, EWT: 1.6 pipeline stages). Neural Networks also performed strongly (accuracy: 87.5%, F1-score: 0.87) but required more resources. Random Forest offered a balance of interpretability and performance (accuracy: 86.2%, F1-score: 0.85). Logistic Regression, though interpretable, underperformed (accuracy: 74.5%, F1-score: 0.71). Statistical analysis confirmed significant differences between models (p < 0.05).

Discussion:
The findings highlight the potential of AI to move CI/CD pipelines from reactive monitoring to proactive failure prevention. Gradient boosting methods such as XGBoost are particularly effective in capturing complex patterns of failure. While challenges of dataset diversity, model explainability, and integration remain, AI-driven prediction can improve developer productivity, reduce wasted compute cycles, and sustain faster release cadences.

Conclusion:
AI-driven build failure prediction enhances CI/CD efficiency by enabling earlier detection of failures and reducing wasted build efforts. XGBoost emerged as the most effective model, though organizations should balance predictive power with explainability when selecting models for integration.