A Hybrid Software Testing Recommendation System Based on Bugs and Tests Descriptions

During the software development cycle, there is often a need to run tests after correcting a bug. The tests to be executed are only a few times automated, demanding even more time from the tester. In addition to the testing time, there is also the difficulty in selecting the tests that should be executed at that moment, as depending on the size of the software, the number of tests reaches thousands. In this research, we propose a hybrid model for recommending manual tests, which, based on the bug description and test steps, selects the best tests to be carried out to check whether a given bug has been resolved. This work was developed using real-world data from software under development, which features manual user interface tests. During development, our application presented an accuracy rate of 60.82% through the Recall@20 metric using the training data, which was confirmed through the use of human users, where the system presented an accuracy rate of 59.70%.