Requirements Business Value and Test Case Attributes based Test Case Prioritization Using Deep Reinforcement Learning.

Testing is a cost-intensive process whereas regression testing is used for the purpose of finding out if there is a regression in a post-evolution or post-maintenance scenario. It is expensive to run all test cases for regression testing and therefore we require a test case selection strategy so that we may select a subset of test cases from a previously available set of all test cases. Test case prioritization is a mechanism that can be used as a test subset selection strategy for regression testing. We investigate how requirements priority and importance as well as test case parameters such as fault severity, fault count, and their inter-dependency can be used for proposing a reward function to improve results. We use a deep reinforcement learning approach to optimize or prioritize the test cases using our reward function. We make use of an off-policy, model-free, and value-based test case prioritization approach considering a deep Q-Network (DQN) agent and a reward system based on a business value of test cases that prioritize the test cases against those business values. We use performance metrics to measure the performance and effectiveness of our research. We have compared our results with the published research and conclude that the results achieved from our approach outperform the previously published results of state-of-the-art studies.