Bug Severity Classification Based on Class-Membership Information

Now-a-days bug classification along with its severity prediction has become an important issue for better maintenance and cost reduction of software. Different approaches have already been introduced in this regard including topic modeling, concept profile etc., however, most of them require a lot of parameter tuning, different types of computations for identifying bug severity. Furthermore, for reduced computational cost many of the state-of-the-art methods use standard Naïve Bayes for classification. However, priors used in this case may degrade the results due to the unavailability of training data. Moreover, cross project performance has rarely been discussed. To address these issues, we introduce a method namely Class-Membership Information of a Term (CMT) that does not use any priors, is computationally simple, free from parameter tuning and performs better compared to the existing methods. Rigorous experiments based on three benchmark datasets demonstrate that CMT on an average performs at most 5% and 12.5% better than other state-of-the-art methods considering within and cross project classification respectively.