Bug tracking is one of the prominent activities during the maintenance phase of software development. The severity of the bug acts as a key indicator of its criticality and impact towards planning evolution and maintenance of various types of software products. This indicator measures how negatively the bug may affect the system functionality. This helps in determining how quickly the development teams need to address the bug for successful execution of the software system. Due to a large number of bugs reported every day, the developers find it really difficult to assign the severity level to bugs accurately. Assigning incorrect severity level results in delaying the bug resolution process. Thus automated systems were developed which will assign a severity level using various machine learning techniques. In this work, five different types of sentence embedding techniques have been applied on bugs description to convert the description comments to an n-dimensional vector. These computed vectors are used as an input of the software defect severity level prediction models and ensemble techniques like Bagging, Random Forest classifier, Extra Trees classifier, AdaBoost and Gradient Boosting have been used to train these models. We have also considered different variants of the Synthetic Minority Oversampling Technique (SMOTE) to handle the class imbalance problem as the considered datasets are not evenly distributed. The experimental results on six projects highlight that the usage of sentence embedding, ensemble techniques, and different variants of SMOTE techniques helps in improving the predictive ability of defect severity level prediction models.

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.