Generating duplicate bug datasets
Alina Lazar, Sarah Ritchey, Bonita Sharif · 2014
Automatic identification of duplicate bug reports is an important research problem in the mining software repositories field. This paper presents a collection of bug datasets collected, cleaned and preprocessed for the duplicate bug report identification problem. The datasets were extracted from open-source systems that use Bugzilla as their bug tracking component and contain all the bugs ever submitted. The systems used are Eclipse, Open Office, NetBeans and Mozilla. For each dataset, we store both the initial data and the cleaned data in separate collections in a mongoDB document-oriented database. For each dataset, in addition to the bug data collections downloaded from bug repositories, the database includes a set of all pairs of duplicate bugs together with randomly selected pairs of non-duplicate bugs. Such a dataset is useful as input for classification models and forms a good base to support replications and comparisons by other researchers. We used a subset of this data to predict duplicate bug reports but the same data set may also be used to predict bug priorities and severity.