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During the development of software and electronic devices, it is inevitable to make mistakes. In large, developed companies, assigning a request to the right development team or even a department is not an easy task. Often, the creation of software bug reports and assignment to groups is also formalized by appropriate processes. The paper presents a novel method of software bug report assignment to a group of developers or analysts. A specific usage of organizational structure at the company is a key component of the proposed approach. There are presented results from real use application including both machine learning predictions and human decisions. Human predictions are not independent, the issues are raised as to why comparing the results of machine learning models with those of humans may be inappropriate and what factors influence human decisions. The work also covers conclusive research about potential benefits of the application of automated assignment of bug reports. [ABSTRACT FROM AUTHOR]

Fault management is an expensive process and analyzing data manually requires a lot of resources. Modern software bug tracking systems may be armed with automated bug report assignment functionality that facilitates bug classification or bug assignment to proper development group. For supporting decision systems, it would be beneficial to introduce information related to explainability. The purpose of this work is to evaluate the use of explainable artificial intelligence (XAI) in processes related to software development and bug classification based on bug reports created by either software testers or software users. The research was conducted on two different datasets. The first one is related to classification of security vs non-security bug reports. It comes from a telecommunication company which develops software and hardware solutions for mobile operators. The second dataset contains a list of software bugs taken from an opensource project. In this dataset the task is to classify issues with one of following labels crash, memory, performance, and security. Studies on XAI-related algorithms show that there are no major differences in the results of the algorithms used when comparing them with others. Therefore, not only the users can obtain results with possible explanations or experts can verify model or its part before introducing into production, but also it does not provide degradation of accuracy. Studies showed that it could be put into practice, but it has not been done so far.