Smart prediction for test smells refactorings

Test smells are considered bad practices for developing the test code. Their presence can reduce the test code quality, thus harming software testing and maintenance activities. Software refactoring has been a key practice to handle smells and improve software quality without changing its behavior. However, existing refactoring tools target production code with very different characteristics than test code. Despite the research invested in test smell refactoring, little is known about whether current refactorings improve the test code quality. This thesis proposal presents our research to help developers decide when and how to refactor test smells through a machine learning-based approach. First, we aim to mine refactorings performed by developers to derive a catalog of test-specific refactorings and their impact on the test code. Our findings show that developers prefer specific features of the testing frameworks, which may lead to test smells such as Inappropriate Assertion and Handling Exception. While the refactorings proposed in the literature aligned with the evolution of testing frameworks helps refactoring test smells, the Inappropriate Assertion remains unexplored in the literature. Second, we aim to collect and learn from test-specific refactorings to suggest which refactorings have the potential to fix test smells. As a result, the approach could support the test smells detection and refactoring guided to the practices closer to the developers’ perspective