University of Hertfordshire

By the same authors

Getting defect prediction into industrial practice: The ELFF tool

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • David Bowes
  • Steve Counsell
  • Tracy Hall
  • Jean Petric
  • Thomas Shippey
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Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 28th International Symposium on Software Reliability Engineering Workshops, ISSREW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages44-47
Number of pages4
ISBN (Electronic)9781538623879
DOIs
Publication statusPublished - 14 Nov 2017
Event28th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2017 - Toulouse, France
Duration: 23 Oct 201726 Oct 2017

Conference

Conference28th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2017
CountryFrance
CityToulouse
Period23/10/1726/10/17

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.

ID: 13400416