A comparative study of artificial neural networks and info-fuzzy networks as automated oracles in software testing

Deepam Agarwal, Dan E. Tamir, Mark Last, Abraham Kandel

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Software quality is one of the main concerns of software users. Hence, software testing is an utterly important phase in the software development life cycle. Nevertheless, manual evaluation of program compliance with its specification may be prohibitively time consuming. As a remedy, several software testing systems are using an automatic oracle to confirm that the developed software complies with its specification and determine whether a given test case exposes faults. The use of artificial neural networks and info-fuzzy networks as automated oracles has been explored elsewhere. Nevertheless, there is not enough research comparing these two popular approaches to automated evaluation of the test outcome. This paper fills the gap and reports on a set of experiments designed to compare the two methods based on ROC curves, training time, and dispersion analysis.

Original languageEnglish
Article number6155611
Pages (from-to)1183-1193
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume42
Issue number5
DOIs
StatePublished - 23 Feb 2012

Keywords

  • Black-box testing
  • clustering techniques
  • dispersion analysis
  • info-fuzzy networks (IFNs)
  • neural networks
  • software testing

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