Abstract
Software defect prediction is one of the most active research areas in software engineering and plays an important role in software quality assurance. In recent years, many new defect prediction studies have been proposed. There are four main aspects of research: machine learning-based prediction algorithms, manipulating the data, effor-softaware prediction and empirical studies. The research community is still facing many challenges in constructing methods, and there are also many research opportunities in the meantime. This paper proposes a method of applying spiking neural network to software defect prediction. The software defect prediction model is constructed by feed-forward spiking neural networks and trained by spike train learning algorithm. This model uses the existing project data sets to predict software defects projects. Extensive experiments on 28 public projects from five data sources indicate that the effectiveness of the proposed approach with respect to the considered metrics.
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Lin, X., Yang, J., Li, Z. (2020). Software Defect Prediction with Spiking Neural Networks. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_75
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DOI: https://doi.org/10.1007/978-3-030-63823-8_75
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