A Study on Machine Learning Applied to Software Bug Priority Prediction

Malhotra, Ruchika; Dabas, Ajay Kumar; Hariharasudhan, A.S.; Pant, Manish

doi:10.1109/confluence51648.2021.9377083

Cited by 16 publications

(1 citation statement)

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“…Machine learning is effective for bug prediction, and several studies have proposed various machine learning models and features for this task. Malhotra et al compare five machine learning classifiers (Multinomial Naive Bayes, Decision Tree, Logistic Regression, Random Forest, and Ad-aBoost) for predicting fault priorities using data from six open-source projects [45]. The authors found that all five classifiers performed well, but Multinomial Naive Bayes achieved the best overall performance.…”

Section: B Issue Tracking Systemsmentioning

confidence: 99%

Accurate Information Type Classification for Software Issue Discussions With Random Oversampling

Muhibullaev,

Kim

2024

IEEE Access

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An Issue Tracking System (ITS) plays crucial roles in software development and provides valuable information to understand issue management. In an ITS, software developers often discuss issues reported during software development. Recent studies analyzed such issue discussions and identified information types of issue comments that appeared in the discussions. Automatic classification of the information types can help developers to more easily understand and locate required information, but existing techniques cannot provide accurate classification. In this study, we propose a more accurate technique to classify information types of issue comments. The key to increasing classification performance is employing random oversampling, to deal with imbalances among training instances of different information types. With random oversampling, we trained a classifier using logistic regression with hyperparameter tuning and achieved an average 0.95 F1-score, which was much higher than 0.53 of the compared existing technique. We also considered other two key aspects of the technique to fully investigate the potential performance improvement. We expanded an existing issue comment dataset by adding 4,098 more instances, which almost doubles the size of the dataset. Also, we analyzed the influence of hyperparameters on classification performance and found that using values within an appropriate range is important to achieve high performance. INDEX TERMSissue discussion analysis, issue management, issue tracking systems, open source software, random oversampling I. INTRODUCTION IN software development, an Issue Track-ing System (ITS) plays an important role in issue management, and any issues during the development can be reported, tracked, and discussed in the system. Because of its crucial roles, many software engineering studies and techniques have targeted ITSs. Some of the stud-

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Section: B Issue Tracking Systemsmentioning

confidence: 99%

Accurate Information Type Classification for Software Issue Discussions With Random Oversampling

Muhibullaev,

Kim

2024

IEEE Access

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Prioritization of Software Bugs Using Entropy‐Based Measures

Kumari,

Singh,

Singh

2024

J Software Evolu Process

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Open‐source software is evolved through the active participation of users. In general, a user request for bug fixing, the addition of new features, and feature enhancements. Due to this, the software repositories are increasing day by day at an enormous rate. Additionally, user distinct requests add uncertainty and irregularity to the reported bug data. The performance of machine learning algorithms drastically gets influenced by the inappropriate handling of uncertainty and irregularity in the bug data. Researchers have used machine learning techniques for assigning priority to the bug without considering the uncertainty and irregularity in reported bug data. In order to capture the uncertainty and irregularity in the reported bug data, the summary entropy–based measure in combination with the severity and summary weight is considered in this study to predict the priority of bugs in the open‐source projects. Accordingly, the classifiers are build using these measures for different machine learning techniques, namely, k‐nearest neighbor (KNN), naïve Bayes (NB), J48, random forest (RF), condensed nearest neighbor (CNN), multinomial logistic regression (MLR), decision tree (DT), deep learning (DL), and neural network (NNet) for bug priority prediction This research aims to systematically analyze the summary entropy–based machine learning classifiers from three aspects: type of machine learning technique considered, estimation of various performance measures: Accuracy, Precision, Recall, and F‐measure and through existing model comparison. The experimental analysis is carried out using three open‐source projects, namely, Eclipse, Mozilla, and OpenOffice. Out of 145 cases (29 products X 5 priority levels), the J48, RF, DT, CNN, NNet, DL, MLR, and KNN techniques give the maximum F‐measure for 46, 35, 28, 11, 15, 4, 3, and 1 cases, respectively. The result shows that the proposed summary entropy–based approach using different machine learning techniques performs better than without entropy‐based approach and also entropy‐based approach improves the Accuracy and F‐measure as compared with the existing approaches. It can be concluded that the classifier build using summary entropy measure significantly improves the machine learning algorithms' performance with appropriate handling of uncertainty and irregularity. Moreover, the proposed summary entropy–based classifiers outperform the existing models available in the literature for predicting bug priority.

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