Machine Learning-Based Test Smell Detection

• URL: http://arxiv.org/abs/2208.07574v1
• Date: Tue, 16 Aug 2022 07:33:15 GMT
• Title: Machine Learning-Based Test Smell Detection
• Authors: Valeria Pontillo, Dario Amoroso d'Aragona, Fabiano Pecorelli, Dario Di Nucci, Filomena Ferrucci, Fabio Palomba
• Abstract summary: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. We propose the design and experimentation of a novel test smell detection approach based on machine learning to detect four test smells.
• Score: 17.957877801382413
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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