Abstract
Advancements in software technology have resulted in a sharp increase in the complexity of software with an equally increasing number of bugs reported every day. Some of these bugs have a high severity and can often lead to significant business impacts. Thus, they need to be resolved by the developers at the earliest. Many of these reports are similar to the bug reports that were reported and resolved in the past. By suggesting similar incidents, the developers can refer to the troubleshooting information, thus effectively reducing the TTM (Time to Mitigate) of the software bugs. The developers also spend a significant amount of time and effort in triaging the bugs into their respective areas. Previous studies have mainly relied on unsupervised learning techniques for the detection of duplicate reports and ignored some key aspects of the bug reports. We conducted comprehensive research on real bugs reported for Microsoft Dynamics 365 Application Software. Our research presents a novel two-phase approach for suggesting similar incidents. The first phase called Binning involves the creation of a labelled dataset for employing a supervised learning algorithm for triaging the software incidents into multiple categories. Thus, the first phase also presents a solution for automating the process of triaging the incidents in addition to the first stage of filtering. The second phase introduces the use of error execution information and acknowledgment information for the calculation of similarity scores which has largely been ignored in the previous studies. The evaluation results show that the precision rate of our proposed approach reaches up to 95.8% while the model achieves recall rates of 67%–93.5%.
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Agrawal, B., Mishra, M., Parashar, V. (2021). A Novel Method for Automated Suggestion of Similar Software Incidents Using 2-Stage Filtering: Findings on Primary Data. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_43
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