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| With the increasing of scale and complexity of the software, the defect in software become unavoidable. Therefore, bug fixing is an essential activity in software maintenance. The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, although there are some rules available on how to justify the level of severity for a given bug, the manual process remains a big problem which can increase fixing time and achieve low precision. To address this issue, we propose GRUModel, a novel model based on neural network that achieve good performance in both prediction accuracy and fixing time. Specifically, we utilize various features not only bug description as input data (e.g., component, developer, priority and severity). To evaluate our approach, we measured the effectiveness of our study by using about 180,000 golden bug reports extracted from five open source products (platform,cdt,jdt,pde and birt). The experiment results demonstrate that our approach predict the severity with a higher accuracy (both precision and recall vary between 0.72-0.85), compared with the existing methods such as Naive Bayes. |
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| Keywords:Software engineering;Various feature; GRU; Neural network; Naive Bayes; |
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