An Empirical Study on Application of Word Embedding Techniques for Prediction of Software Defect Severity Level
Lov Kumar, Mukesh Kumar, Lalita Bhanu Murthy, Sanjay Misra, Vipul Kocher, Srinivas Padmanabhuni
Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 477–484 (2021)
Abstract. This work aims to develop defect severity level prediction models that have the ability to assign severity level of defects based on bugs report. In this work, seven different word embedding techniques are applied to defect description to represent the word, not just as a number but as a vector in n-dimensional space. Further, three feature selection techniques have been applied to find the right set of relevant vectors. The effectiveness of these word embedding techniques and different sets of vectors are evaluated using different classification techniques with SMOTE to overcome the class imbalance problem.
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