Abstract
Branch coverage prediction plays a critical role in achieving high effective performance for modern applications. However, traditional test solutions are often inadequate to meet test objectives. If the testers know the branch coverage prediction achieved by any specific tool, they can test a subset of classes instead of the complete one. It is noticed that earlier test information of the tools can help make appropriate decisions about branch coverage tool selection. This paper examines the possibility of using source code metrics for branch coverage prediction. We considered different features extracted from 3105 java classes. We considered machine learning techniques like “random forest” (RF), “support vector regression” (SVR), and “linear regression” (LR). We also investigate performance using our ensemble model. The obtained results show that the ensemble model achieved an average of 0.12 and 0.19 “mean absolute error” (MAE) on testing with EVOSUITE and RANDOOP, respectively.
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Barisal, S.K., Kishore, P., Nayak, G. (2022). Source Code Features Based Branch Coverage Prediction Using Ensemble Technique. In: Mohanty, M.N., Das, S., Ray, M., Patra, B. (eds) Meta Heuristic Techniques in Software Engineering and Its Applications. METASOFT 2022. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-11713-8_2
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DOI: https://doi.org/10.1007/978-3-031-11713-8_2
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