Furthermore, they improve the user experience of the software and update it for new end-user requirements. In addition to software testing, bugs reported by end-users should be explored because bug reports reveal bugs that were undetected in the software-testing phase. Many efforts are directed at automatic bug detection using software testing approaches, such as static and dynamic testing, white and black box testing, and other testing strategies. In addition, a statistical analysis shows that the results are reliable and can be generalized to other datasets or similar classifiers.Ĭurrently, a significant time- and cost-consuming phase of software engineering is software maintenance, where finding and handling the bugs and managing the changes are the most critical tasks. Without using the longest common subsequence (LCS) feature, which is effective but time-consuming, our proposed features could cover the effectiveness of LCS with lower time-complexity and runtime overhead. The results also showed that the combination of all types of features could improve the validation performance of DBRD even for the LR classifier with less validation performance, which can be implemented easily for software bug triage systems. The results showed that our proposed model is more effective both for the datasets for which state-of-the-art approaches were effective (i.e., Mozilla Firefox) and those for which state-of-the-art approaches were less effective (i.e., Android). The pre-processing methods (primarily stemming) could improve the validation performance of DBRD slightly (up to 0.3%), but rule-based machine learning algorithms are more useful for the DBRD problem. Our proposed features improved the validation performance of DBRD concerning runtime performance.
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