Boosting Test Smell Prediction using Deep Learning

Abstract

Test smells are indicative symptoms of poor design choices in test code, potentially reducing maintainability and compromising test effectiveness. While machine learning-based methods have been proposed to automate test smell detection, their predictive performance is still limited. Deep learning offers a promising solution due to its ability to learn complex context and patterns from data. However, its application to test smell prediction, particularly with sequence data extracted from test code, remains underexplored. To address these motivations, this study aims to present a deep learning-based approach for test smell prediction using input data in the form of sequences. The proposed method is experimentally evaluated on two popular test smells: Eager Test and Mystery Guest. The performance of all proposed models demonstrated significant improvement over baseline models, with the highest F1-score increase of approximately 24%. A comparative evaluation of three deep learning models, including Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, reveals that Bidirectional Long Short-Term Memory achieved the highest F1-score of 0.7475 for Eager Test, while Convolutional Neural Network performed best on Mystery Guest with F1-score of 0.6529. This work is considered the first effective application of deep learning for predicting test smell on sequence data, highlighting the promising approach in the area.