An End-to-End Test Case Prioritization Framework using Optimized Machine Learning Models

Regression testing in software development is challenging due to the large number of test cases and continuous integration (CI) practices. Recently, test case prioritization (TCP) using machine learning (ML) has been shown to efficiently execute regression tests. This study introduces an automated, endto-end, self-contained ML-based framework, TCP-Tune, tailored exclusively for TCP. The framework utilizes open-source version control system data to combine code-change-related features with test execution results. This integration allows the automated optimization of hyperparameters across different ML models to improve the TCP. The framework also effectively visualizes and utilizes multiple evaluation metrics to evaluate the performance of the model over several builds. Unlike existing implementations, which rely on various frameworks, TCP-Tune enables the effortless incorporation of features from multiple sources and fine-tuned models, thereby providing optimum test prioritization in the ever-changing field of software development. Our approach has helped to provide efficient TCP through experimental assessments of a real-life, large-scale CI system.