Rights statement: © 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
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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - So You Need More Method Level Datasets for Your Software Defect Prediction?
T2 - 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2016
AU - Shippey, Thomas
AU - Hall, Tracy
AU - Counsell, Steve
AU - Bowes, David
N1 - © 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
PY - 2016/9/8
Y1 - 2016/9/8
N2 - 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.
AB - 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.
KW - Boa
KW - Data Mining
KW - Defect linking
KW - Defect Prediction
KW - Defects
U2 - 10.1145/2961111.2962620
DO - 10.1145/2961111.2962620
M3 - Conference contribution/Paper
AN - SCOPUS:84991583839
BT - ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement
PB - IEEE Computer Society
CY - New York
Y2 - 8 September 2016 through 9 September 2016
ER -