Rediscovery Datasets: Connecting Duplicate Reports

The same defect can be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. In the case of popular open source software, high volume of defects is reported on a regular basis. A large number of these reports are actually duplicates / rediscoveries of each other. Researchers have analyzed the factors related to the content of duplicate defect reports in the past. However, some of the other potentially important factors, such as the inter-relationships among duplicate defect reports, are not readily available in defect tracking systems such as Bugzilla. This information may speed up bug fixing, enable efficient triaging, improve customer profiles, etc. In this paper, we present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects. We believe that sharing these data with the community will help researchers and practitioners to better understand the nature of defect rediscovery and enhance the analysis of defect reports.

[1]  Edward N. Adams,et al.  Optimizing Preventive Service of Software Products , 1984, IBM J. Res. Dev..

[2]  Andriy V. Miranskyy,et al.  Metrics of Risk Associated with Defects Rediscovery , 2011, ArXiv.

[3]  Emden R. Gansner,et al.  An open graph visualization system and its applications to software engineering , 2000, Softw. Pract. Exp..

[4]  Daniel M. Germán,et al.  Towards a simplification of the bug report form in eclipse , 2008, MSR '08.

[5]  Andriy V. Miranskyy,et al.  Selection of customers for operational and usage profiling , 2009, DBTest '09.

[6]  Per Runeson,et al.  Detection of Duplicate Defect Reports Using Natural Language Processing , 2007, 29th International Conference on Software Engineering (ICSE'07).

[7]  Eleni Stroulia,et al.  A contextual approach towards more accurate duplicate bug report detection and ranking , 2013, Empirical Software Engineering.

[8]  David Lo,et al.  DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis , 2013, ICSM.

[9]  Westley Weimer,et al.  Modeling bug report quality , 2007, ASE '07.

[10]  Thomas Zimmermann,et al.  Duplicate bug reports considered harmful … really? , 2008, 2008 IEEE International Conference on Software Maintenance.

[11]  Serge Demeyer,et al.  The Eclipse and Mozilla defect tracking dataset: A genuine dataset for mining bug information , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[12]  Gail C. Murphy,et al.  Who should fix this bug? , 2006, ICSE.