Bug Severity Prediction Based on GRU and Various Features
首发时间:2018-04-20
Abstract: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.
keywords: Software engineering Various feature GRU Neural network Naive Bayes
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基于GRU神经网络和多特征的bug严重程度预测模型
摘要:随着软件规模和复杂性的增加,软件的缺陷变得不可避免。因此,bug修复是软件维护中必不可少的活动。报告缺陷的严重程度是决定需要修复顺序的关键因素。尽管有一些关于如何判定给定bug的严重程度的规则,但是人工操作的过程会增加修复时间和低精度的预测。为了解决这一问题,我们提出了基于GRU神经网络的模型,该模型在预测精度和减少修复时间方面都取得了良好的效果。具体地说,我们利用不仅仅是bug报告的描述,还包括其它属性,作为输入数据(例如组件、开发人员、优先级和严重性)。为了评估我们的方法,我们使用了从bugzilla网站中提取的5个开源产品(platform、cdt、jdt、pde和birt)总共180,000个bug报告来度量我们的研究的有效性。实验结果表明,与朴素贝叶斯等现有方法相比,我们的方法预测精度较高(精确度和召回率均在0.72-0.85之间)。
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