Journal
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
Volume 29, Issue 2, Pages 139-175Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218194019500074
Keywords
Bug reports; extreme learning machine; imbalanced distribution; severity; software application testing
Categories
Funding
- National Natural Science Foundation of China [61672122, 61402070, 61602077]
- Natural Science Foundation of Liaoning Province of China [2015020023]
- Educational Commission of Liaoning Province of China [L2015060]
- Fundamental Research Funds for the Central Universities [3132016348, 3132017125]
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Manually inspecting bugs to determine their severity is often an enormous but essential software development task, especially when many participants generate a large number of bug reports in a crowdsourced software testing context. Therefore, boosting the capabilities of methods of predicting bug report severity is critically important for determining the priority of fixing bugs. However, typical classification techniques may be adversely affected when the severity distribution of the bug reports is imbalanced, leading to performance degradation in a crowdsourcing environment. In this study, we propose an enhanced oversampling approach called CR-SMOTE to enhance the classification of bug reports with a realistically imbalanced severity distribution. The main idea is to interpolate new instances into the minority category that are near the center of existing samples in that category. Then, we use an extreme learning machine (ELM) - a feedforward neural network with a single layer of hidden nodes - to predict the bug severity. Several experiments were conducted on three datasets from real bug repositories, and the results statistically indicate that the presented approach is robust against real data imbalance when predicting the severity of bug reports. The average accuracies achieved by the ELM in predicting the severity of Eclipse, Mozilla, and GNOME bug reports were 0.780, 0.871, and 0.861, which are higher than those of classifiers by 4.36%, 6.73%, and 2.71%, respectively.
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