JOURNAL ARTICLE

Mutation-Based Minimal Test Suite Generation for Boolean Expressions.

  • Published In: International Journal of Software Engineering & Knowledge Engineering, 2023, v. 33, n. 6. P. 865 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Ayav, Tolga; Belli, Fevzi 3 of 3

Abstract

Boolean expressions are highly involved in control flows of programs and software specifications. Coverage criteria for Boolean expressions aim at producing minimal test suites to detect software faults. There exist various testing criteria, efficiency of which is usually evaluated through mutation analysis. This paper proposes an integer programming-based minimal test suite generation technique relying on mutation analysis. The proposed technique also takes into account the cost of fault detection. The technique is optimal such that the resulting test suite guarantees to detect all the mutants under given fault assumptions, while maximizing the average percentage of fault detection of a test suite. Therefore, the approach presented can also be considered as a reference method to check the efficiency of any common technique. The method is evaluated using four well-known real benchmark sets of Boolean expressions and is also exemplary compared with MCDC criterion. The results show that the test suites generated by the proposed method provide better fault coverage values and faster fault detection. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Software Engineering & Knowledge Engineering. 2023/06, Vol. 33, Issue 6, p865
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:0218-1940
  • DOI:10.1142/S0218194023500183
  • Accession Number:164558277
  • Copyright Statement:Copyright of International Journal of Software Engineering & Knowledge Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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