Does bug report summarization help in enhancing the accuracy of bug severity classification?

Abstract

The programmer cannot write a program without any bug. A large numbers of bugs are deposited into the bug tracking system through bug reports. To find the root cause of a bug, a meaningful and huge conversation happens between the developer and reporter. The developer (triager) reads the whole bug report and then classified according to severity. The previous researchers observed that the bug report summaries provide the more resourcefully investigate information in the bug repository to the developer as part of the severity classification task. To further investigate the relationship between bug report summary and bug severity classification. A novel approach is proposed by using swarm intelligence and machine learning approaches. Firstly the n-gram technique is used to extract the semantic features score. These features are fed into the Summary Subset Selection Phase to select the optimal summary subset. The selected subset features are fed into the feature scoring phase to provide a relative score to each feature. These optimized features are used to train the proposed model. At last Naive Bayes approach is used to classify the multiclass severity classification. The results are analyzed by using 10-fold cross-validation on three benchmark datasets showed better performance in terms of Precision, Recall and F-measure. It is observed that the performance depend on the bug report contents. If the bug report has larger data for summarization than the summarization increase the classification accuracy otherwise decrease.