An Empirical Study on Application of Word Embedding Techniques for Prediction of Software Defect Severity Level

Software defect severity level helps to indicate the impact of bugs on the execution of the software and how rapidly these bugs need to be addressed by the team. The working team is regularly analyzing the bugs report and prioritizing the defects. The manual prioritization of these defects based on the experience may be an inaccurate prediction of the severity that will delay in fixing of critical bugs. It is compulsory to automate the process of assigning an appropriate level of severity based on bug report results with an objective to fix critical bugs without any delay. 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 in order to reduce the number of features. Since the predictive ability of the developed models depends on the vectors extracted from text as they are used as an input to the defect severity level prediction models. 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 eleven different classification techniques with Synthetic Minority Oversampling Technique (SMOTE) to overcome the class imbalance problem. The experimental results show that the word embedding, feature selection techniques and SMOTE have the ability to predict the severity level of the defect in a software.