Varying defect prediction approaches during project evolution - A preliminary investigation.

Salvatore Geremia, Damian A. Tamburri

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Defect prediction approaches use various features of software product or process to prioritize testing, analysis and general quality assurance activities. Such approaches require the availability of project's historical data, making them inapplicable in early phase. To cope with this problem, researchers have proposed cross-project and even cross-company prediction models, which use training material from other projects to build the model. Despite such advances, there is limited knowledge of how, as the project evolves, it would be convenient to still keep using data from other projects, and when, instead, it might become convenient to switch towards a local prediction model. This paper empirically investigates, using historical data from four open source projects, on how the performance of various kinds of defect prediction approaches-within-project prediction, local and global cross-project prediction, and mixed (injected local cross) prediction-varies over time. Results of the study are part of a long-Term investigation towards supporting the customization of defect prediction models over projects' history.

Original languageEnglish
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 29 May 2018

Bibliographical note

DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Keywords

  • Defect prediction
  • empirical study
  • global prediction
  • local prediction

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