Modern CI/CD pipelines rely on automated testing to maintain software quality and accelerate releases. However, inefficiencies such as flaky tests, redundant executions, and lack of actionable insights often hinder the process, leading to wasted resources, delays, and maintenance overhead. This paper proposes leveraging artificial intelligence (AI) to address these challenges by delivering insights into test stability, prioritization and failure analysis.

This idea centers on analysing historical test execution data using AI models to classify tests as stable, flaky or high-priority. This solution can optimize test suite execution by skipping redundant stable tests and focusing on those prone to failure.

This solution can optimize test suite execution. Machine learning models identify patterns linking test failures to code changes, helping teams address root causes efficiently.

The methodology integrates AI models into build pipelines for dynamic test selection and predictive failure analysis, using metrics like reduced execution time, improved defect detection, and fewer flaky tests to evaluate success.
Challenges such as inconsistent data quality, test environment variability, and overfitting are addressed with solutions like data augmentation, containerized environments, and model regularization. Transparency tools like SHAP or LIME ensure AI-driven decisions are interpretable and actionable for teams.

This framework transforms test automation by automating failure prioritization and root cause analysis, reducing maintenance burdens and accelerating release cycles. The methodology lays the groundwork for future innovations, such as self-healing scripts and cross-project insights, offering a significant leap towards intelligent, efficient pipelines.

Key Takeaways:
• Challenges in Test Automation:
o Flaky tests, redundant executions and lack of failure insights slow down CI/CD pipelines.
• AI-Powered Solution:
o Analyse historical test data to classify tests and optimize test suite execution.
o Prioritize tests prone to failure and skip redundant stable ones.
o Use machine learning to correlate test failures with code changes for root cause analysis.
• Methodology:
o Implement artificial intelligence models for dynamic test selection and predictive failure analysis.
o Integrate into CI/CD pipelines for real-time impact.
• Challenges :
o These models require high-quality, clean, and structured data for accurate analysis and predictions
o Test environments often change due to differences in configurations, hardware, software versions or network conditions.
• Solutions:
o Standardizing and cleaning the data
o Use tools like Docker, Kubernetes to stabilise environments
• Impact:
o Reduced execution time, improved defect detection, and fewer flaky tests.
o Future potential for self-healing tests and cross-project insights.