Abstract
In software development processes, classifying software bugs is a vital step since it helps grasp the nature, implications, and causes of software failures. Further, categorization enables reacting to software bugs appropriately and faster. However, manual classification of software bugs is inefficient and costly, especially in large-scale software projects, since one must deal with extensive bug reports from multiple sources. Hence, many studies have addressed this problem by automated software bug classification with the help of machine learning techniques. Researchers used various machine learning-based algorithms and techniques to obtain better classification performance. Furthermore, many researchers used open source bug repositories to compare their results with previous studies. In this paper, we aimed to report the main studies in machine learning-based automated software bug report classification by highlighting the recent improvements and indicating the key steps in this process. So, this survey can benefit the researchers and practitioners working in automated software bug report classification and other related domains.
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