Abstract
New software systems have been continuously developed and maintained to be useful and satisfy changing needs. Bug fixing is a fundamental activity in such constant software development and maintenance. If it is possible to predict bug severity accurately, it will be a huge contribution toward effective software development. Thus, activity in support of bug severity prediction is important for software development. If bug severity is predicted incorrectly, it brings about increased workloads for developers and increased expense. Therefore, in this study we present a method to improve prediction of bug severity. In this method we added text and meta-fields of the bug reports for our classifier model, and included ‘normal’ severity bug reports which represent a large amount of total reported bugs. The use of added fields and normal bugs were not taken into consideration in other studies. The bugs used in the experiments we performed cover large parts of the bug reports available in open source projects such as Mozilla and Eclipse. The experiments showed that the prediction accuracy was improved by the method we present.
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