Software bug severity and priority prediction using SMOTE and intuitionistic fuzzy similarity measure

A software bug tracking system receives several bug reports in a rapid manner during the maintenance of software. In order to fix the important and urgent bugs, the triager has to assign severity and priority to individual bugs on time. However, there are a lot of uncertainties in the bug reports due to bias, noise, and abnormal data. At the same time, the presence of common terms in multiple severity and priority classes creates confusion in the mind of the triager. Furthermore, machine learning and deep learning approaches generally belong to discriminative learning with a clear-cut outcome. Instances of software bug reports are textual in nature. As a result, these are fuzzy and cannot be classified with a clear-cut outcome. To overcome the above problems, in this paper, an Intuitionistic Fuzzy Similarity Measure (IFSM) based severity prediction technique (IFSMSP) and priority prediction technique (IFSMPP) are proposed for predicting the severity and priority of a new bug by using already labeled bugs. Initially, the Synthetic Minority Oversampling Technique (SMOTE) is used to balance the severity and priority label of software bugs. Then the severity-term dictionary or priority-term dictionary is created by extracting the most frequent terms from the bug summary using text mining and Natural Language Processing (NLP). Then the data is represented using an intuitionistic fuzzy set (IFS) by calculating the membership, non-membership, and hesitancy degrees. Then 15 different IFSM techniques are investigated for predicting the severity and priority of software bugs. Experiments are carried out on large software bug repositories (Eclipse, Mozilla, Apache, and NetBeans) with a 10-fold cross-validation technique. IFSMSP outperformed other state-of-the-art priority models by obtaining an accuracy of 92.3%, 90.6%, 91.9%, and 91.2%, and IFSMPP outperformed other state-of-the-art models by obtaining an accuracy of 93.2%, 91.9%, 92.7%, and 92.3% on the Eclipse, Mozilla, Apache, and NetBeans software bug repositories, respectively.