Test case generation and reduction by automated input-output analysis

Prachi Saraph, Mark Last, Abraham Kandell

Research output: Contribution to journalConference articlepeer-review

19 Scopus citations

Abstract

In the software testing process, selecting the test cases and verifying their results requires a lot of subjective decisions and human intervention. For a program having a large number of inputs, the number of corresponding combinatorial black-box test cases is huge. A method needs to be established in order to limit the number of test cases and to choose the most important ones. In this research effort we present a novel methodology for identifying important test cases automatically. These test cases involve input attributes which contribute to the value of an output and hence are significant. The reduction in the number of test cases is attributed to identifying input-output relationships. A ranked list of features and equivalence classes for input attributes of a given code are the main outcomes of this methodology. Reducing the number of test cases results directly in the saving of software testing resources.

Original languageEnglish
Pages (from-to)768-773
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
StatePublished - 24 Nov 2003
EventSystem Security and Assurance - Washington, DC, United States
Duration: 5 Oct 20038 Oct 2003

Keywords

  • Artificial neural networks
  • Feature ranking
  • Input-output analysis
  • Rule-extraction
  • Software testing
  • Test cases

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