An artificial intelligence framework on software bug triaging, technological evolution, and future challenges: A review

The timely release of defect-free software and the optimization of development costs depend on efficient software bug triaging (SBT) techniques. SBT can also help in managing the vast information available in software bug repositories. Recently, Artificial Intelligence (AI)-based emerging technologies have been utilized excessively, however, it is not clear how it is shaping the design, development, and performance in the field of SBT. It is therefore important to write this well-planned, comprehensive, and timely needed AI-based SBT review, establishing clear findings. For selecting the key studies in SBT, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) analysis was carried out, and 123 studies were selected for the AI-based review, addressing key research questions. Further, Cochrane protocol was applied for risk-of-bias computations for selecting AI techniques. We studied the six types of software bug triaging techniques (SBTT) that were analyzed. AI has provided the possibility of automating the time-consuming manual SBT process. Our study shows that AI-based architectures, developers for newly reported bugs can be identified more accurately and quickly. Deep learning (DL)-based approaches demonstrate capabilities for developing SBT systems having improved (i) learning rate, (ii) scalability, and (iii) performance as compared to conventional approaches. For evaluating the SBT techniques, apart from the accuracy, precision, and recall, the mean average precision (mAP) is suggested to be an effective metric. In the future, more work is expected in the direction of SBT considering additional information from developer's networks, other repositories, and modern AI technologies