2022/08/16 by Valeria Pontillo, Dario Amoroso d'Aragona, Pontillo, Valeria +9 · 2 citations
Computer Science · #FOS: Computer and information sciences #Information and Cyber Security #Machine Learning (cs.LG) #Software Engineering (cs.SE) #Software Engineering Research #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2208.07574
openalex publication_date 2022/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of such detectors is still limited and dependent on thresholds to be tuned. Objective: We propose the design and experimentation of a novel test smell detection approach based on machine learning to detect four test smells. Method: We plan to develop the largest dataset of manually-validated test smells. This dataset will be leveraged to train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we plan to compare our approach with state-of-the-art heuristic-based techniques.