So You Need More Method Level Datasets for Your Software Defect Prediction?:Voilà!

Shippey, Thomas and Hall, Tracy and Counsell, Steve and Bowes, David (2016) So You Need More Method Level Datasets for Your Software Defect Prediction?:Voilà! In: ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. IEEE Computer Society, ESP. ISBN 9781450344272

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Abstract

Context: Defect prediction research is based on a small number of defect datasets and most are at class not method level. Consequently our knowledge of defects is limited. Identifying defect datasets for prediction is not easy and extracting quality data from identified datasets is even more difficult. Goal: Identify open source Java systems suitable for defect prediction and extract high quality fault data from these datasets. Method: We used the Boa to identify candidate open source systems. We reduce 50,000 potential candidates down to 23 suitable for defect prediction using a selection criteria based on the system's software repository and its defect tracking system. We use an enhanced SZZ algorithm to extract fault information and calculate metrics using JHawk. Result: We have produced 138 fault and metrics datasets for the 23 identified systems. We make these datasets (the ELFF datasets) and our data extraction tools freely available to future researchers. Conclusions: The data we provide enables future studies to proceed with minimal effort. Our datasets significantly increase the pool of systems currently being used in defect analysis studies.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© ACM, 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement http://dx.doi.org/10.1145/2961111.2962620
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1712
Subjects:
ID Code:
127416
Deposited By:
Deposited On:
12 Sep 2018 14:06
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Sep 2020 06:04