Regularities in learning defect predictors

Burak Turhan, Ayse Bener, Tim Menzies

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

10 Citations (Scopus)


Collecting large consistent data sets of real world software projects from a single source is problematic. In this study, we show that bug reports need not necessarily come from the local projects in order to learn defect prediction models. We demonstrate that using imported data from different sites can make it suitable for predicting defects at the local site. In addition to our previous work in commercial software, we now explore open source domain with two versions of an open source anti-virus software (Clam AV) and a subset of bugs in two versions of GNU gcc compiler, to mark the regularities in learning predictors for a different domain. Our conclusion is that there are surprisingly uniform assets of software that can be discovered with simple and repeated patterns in local or imported data using just a handful of examples.

Original languageEnglish
Title of host publicationProduct-Focused Software Process Improvement - 11th International Conference, PROFES 2010, Proceedings
Number of pages15
Publication statusPublished - 16 Aug 2010
Externally publishedYes
Event11th International Conference on Product-Focused Software Process Improvement, PROFES 2010 - Limerick, Ireland
Duration: 21 Jun 201023 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6156 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Product-Focused Software Process Improvement, PROFES 2010


  • Code metrics
  • Cross-company
  • Defect prediction
  • Software quality

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