ML-Powered Test Case Prioritisation: Leveraging Code Changes for Optimal Testing

The increasing number of test cases and limited time frames often prevent the execution of complete regression test suites following software changes. This paper addresses this challenge in an industrial software testing context, focusing on daily system tests at Siemens Healthineers. We propose a machine learning-based approach for test case prioritisation that ranks test cases according to their relevance to recent source code changes. Two models, a neural network and a random forest, are developed and evaluated using a dataset comprising over 10,000 test case executions. The experimental results show that both models outperform traditional prioritisation techniques, achieving up to a 50