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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 - Getting defect prediction into industrial practice
T2 - 28th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2017
AU - Bowes, David
AU - Counsell, Steve
AU - Hall, Tracy
AU - Petric, Jean
AU - Shippey, Thomas
N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - Defect prediction has been the subject of a great deal of research over the last two decades. Despite this research it is increasingly clear that defect prediction has not transferred into industrial practice. One of the reasons defect prediction remains a largely academic activity is that there are no defect prediction tools that developers can use during their day-to-day development activities. In this paper we describe the defect prediction tool that we have developed for industrial use. Our ELFF tool seamlessly plugs into the IntelliJ IDE and enables developers to perform regular defect prediction on their Java code. We explain the state-of-art defect prediction that is encapsulated within the ELFF tool and describe our evaluation of ELFF in a large UK telecommunications company.
AB - Defect prediction has been the subject of a great deal of research over the last two decades. Despite this research it is increasingly clear that defect prediction has not transferred into industrial practice. One of the reasons defect prediction remains a largely academic activity is that there are no defect prediction tools that developers can use during their day-to-day development activities. In this paper we describe the defect prediction tool that we have developed for industrial use. Our ELFF tool seamlessly plugs into the IntelliJ IDE and enables developers to perform regular defect prediction on their Java code. We explain the state-of-art defect prediction that is encapsulated within the ELFF tool and describe our evaluation of ELFF in a large UK telecommunications company.
KW - Defect prediction
KW - Industry
KW - Machine learning
KW - Metrics
KW - Tool
U2 - 10.1109/ISSREW.2017.11
DO - 10.1109/ISSREW.2017.11
M3 - Conference contribution/Paper
AN - SCOPUS:85040542929
SP - 44
EP - 47
BT - Proceedings - 2017 IEEE 28th International Symposium on Software Reliability Engineering Workshops, ISSREW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 October 2017 through 26 October 2017
ER -