Abstract
Bug is a defect in software which needs to be identified early so as to avoid unnecessary burden caused by it later. Bug discovery from software modules has been around. However, of late, machine learning (ML) became a useful and appropriate solution to many real-world problems. In this context, usage of machine learning has become an important step forward in improving state of the art in bug detection. It is an artificial intelligence-based (AI) approach that makes it more effective due to the bulk of software modules. Many existing methods strived to incorporate ML for bug discovery. However, there is need for improvement with appropriate methodology. In this paper, we proposed a methodology that exploits two ML techniques known as decision tree (DT) and random forest (RF) for efficient means of discovering bugs from software modules. An empirical study is made using Python data science platform. Experimental results showed that RF performs better than DT in terms of accuracy of bug prediction.
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Ramesh, G., Reddy, K.S.S., Ramu, G., Reddy, Y.C.A.P., Somasekar, J. (2023). An Empirical Study on Discovering Software Bugs Using Machine Learning Techniques. In: Buyya, R., Hernandez, S.M., Kovvur, R.M.R., Sarma, T.H. (eds) Computational Intelligence and Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-19-3391-2_14
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DOI: https://doi.org/10.1007/978-981-19-3391-2_14
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