摘要：Context: Predicting the priority of bug reports is an important activity in software maintenance.Bug priority refers to the order in which a bug or defect should be resolved. A huge number of bugreports are submitted every day. Manual filtering of bug reports and assigning priority to eachreport is a heavy process, which requires time, resources, and expertise. In many cases mistakeshappen when priority is assigned manually, which prevents the developers from finishing theirtasks, fixing bugs, and improve the quality.Objective: Bugs are widespread and there is a noticeable increase in the number of bug reportsthat are submitted by the users and teams’ members with the presence of limited resources, whichraises the fact that there is a need for a model that focuses on detecting the priority of bug reports,and allows developers to find the highest priority bug reports.This paper presents a model that focuses on predicting and assigning a priority level (high or low)for each bug report.Method: This model considers a set of factors (indicators) such as component name, summary,assignee, and reporter that possibly affect the priority level of a bug report. The factors areextracted as features from a dataset built using bug reports that are taken from closed-sourceprojects stored in the JIRA bug tracking system, which are used then to train and test theframework. Also, this work presents a tool that helps developers to assign a priority level for thebug report automatically and based on the LSTM’s model prediction.Results: Our experiments consisted of applying a 5-layer deep learning RNN-LSTM neuralnetwork and comparing the results with Support Vector Machine (SVM) and K-nearest neighbors(KNN) to predict the priority of bug reports.The performance of the proposed RNN-LSTM model has been analyzed over the JIRA dataset withmore than 2000 bug reports. The proposed model has been found 90% accurate in comparison withKNN (74%) and SVM (87%). On average, RNN-LSTM improves the F-measure by 3% comparedto SVM and 15.2% compared to KNN.Conclusion: It concluded that LSTM predicts and assigns the priority of the bug more accuratelyand effectively than the other ML algorithms (KNN and SVM). LSTM significantly improves theaverage F-measure in comparison to the other classifiers. The study showed that LSTM reportedthe best performance results based on all performance measures (Accuracy = 0.908, AUC = 0.95,F-measure = 0.892).
关键词：Assigning; Priority; Bug Tracking Systems; Bug Priority; Bug Severity; Closed-Source; Data Mining; Machine Learning (ML); Deep Learning; RNN-LSTM; SVM; KNN
