Getting defect prediction into industrial practice : The ELFF tool

Bowes, David and Counsell, Steve and Hall, Tracy and Petric, Jean and Shippey, Thomas (2017) Getting defect prediction into industrial practice : The ELFF tool. In: Proceedings - 2017 IEEE 28th International Symposium on Software Reliability Engineering Workshops, ISSREW 2017 :. Institute of Electrical and Electronics Engineers Inc., FRA, pp. 44-47. ISBN 9781538623879

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Abstract

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.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©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.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1712
Subjects:
?? defect predictionindustrymachine learningmetricstoolsoftwaresafety, risk, reliability and quality ??
ID Code:
127412
Deposited By:
Deposited On:
13 Sep 2018 08:52
Refereed?:
Yes
Published?:
Published
Last Modified:
09 Jan 2024 00:38