Prioritizing automated test cases of Web applications using reinforcement learning: an enhancement

Test prioritization helps reduce the time needed to perform testing on the target application under test. It is even more critical when there are lots of tests to be tested within a short period. This paper presents a test prioritization method that enhances our previous method for prioritizing automated tests of Web-based applications using reinforcement learning. The main improvements are focused on the reward function of reinforcement learning and the graph merge-discount factor. We evaluate our method and other six recent test prioritization methods using eleven data sets. The results show that the proposed method outperforms the other methods on most data sets.