Using learning styles of software professionals to improve their inspection team performance

Anurag Goswami, Gursimran Walia, Abhinav Singh

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Inspections of software artifacts during early software development aids managers to detect early faults that may be hard to find and fix later. Results showed inspection ability does not depend on educational background and technical knowledge. This paper presents the results from an industrial empirical study, wherein the Learning Styles (i.e. ability to perceive and process information) of individual inspectors were manipulated to measure its impact on the fault detection effectiveness of inspection teams. Using inspection data from professional developers, we developed virtual teams with varying LS's of individual inspectors and analyzed the team performance. The results from the current study show that teams of inspectors with diverse LS's are significantly more effective at detecting faults as compared to teams of inspectors with similar LS's. Therefore, LS's can aid software managers to create high performance inspection team(s) and manage software quality.

Original languageEnglish (US)
Pages (from-to)1721-1726
Number of pages6
JournalInternational Journal of Software Engineering and Knowledge Engineering
Issue number9-10
StatePublished - Nov 1 2015
Externally publishedYes


  • Learning style
  • Requirements
  • Software inspection

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence


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