Using data mining for automated software testing

M. Last, M. Friendman, A. Kandel

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

In today's software industry, the design of test cases is mostly based on human expertise, while test automation tools are limited to execution of pre-planned tests only. Evaluation of test outcomes is also associated with a considerable effort by human testers who often have imperfect knowledge of the requirements specification. Not surprisingly, this manual approach to software testing results in heavy losses to the world's economy. In this paper, we demonstrate the potential use of data mining algorithms for automated modeling of tested systems. The data mining models can be utilized for recovering system requirements, designing a minimal set of regression tests, and evaluating the correctness of software outputs. To study the feasibility of the proposed approach, we have applied a state-of-the-art data mining algorithm called Info-Fuzzy Network (IFN) to execution data of a complex mathematical package. The IFN method has shown a clear capability to identify faults in the tested program.

Original languageEnglish
Pages (from-to)369-393
Number of pages25
JournalInternational Journal of Software Engineering and Knowledge Engineering
Volume14
Issue number4
DOIs
StatePublished - 1 Aug 2004

Keywords

  • Automated software testing
  • Data mining
  • Finite element solver
  • Info-fuzzy networks
  • Input-output analysis
  • Regression testing

ASJC Scopus subject areas

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

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