Abstract
The most significant feature of a software is its quality. Software defect prediction has grown significantly in popularity over the past several years and may directly impact quality. Defective software modules significantly lower software quality, which results in cost overruns, missed deadlines, and far higher maintenance expenses. One of the best strategies in this direction is to predict software problems using machine learning (ML) methods. In this study, ML algorithms used in this inquiry include artificial neural networks (ANNs), random forest (RF), random tree (RT), decision table (DT), linear regression (LR), Gaussian processes (GP), SMOreg, and M5P. For the purpose of predicting future software defects, a novel software defect prediction model is put forth. The defect prediction is grounded in the past. The outcomes demonstrated that a combination of ML techniques might be used to anticipate software problems in an accurate manner over other algorithms in terms of defect prediction accuracy.
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Kethireddy, J., Aravind, E., Kamal, M.V. (2023). Software Defects Prediction Using Machine Learning Algorithms. In: Reddy, V.S., Prasad, V.K., Wang, J., Rao Dasari, N.M. (eds) Intelligent Systems and Sustainable Computing. ICISSC 2022. Smart Innovation, Systems and Technologies, vol 363. Springer, Singapore. https://doi.org/10.1007/978-981-99-4717-1_10
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