Skip to main content
placeholder image

On adaptive random testing through iterative partitioning

Journal Article


Abstract


  • Random Testing (RT) is an important and fundamental approach to testing computer

    software. Adaptive Random Testing (ART) has been proposed to improve the faultdetection

    capability of RT. ART employs the location information of successful test

    cases (those that have been executed but not revealed a failure) to enforce an even spread

    of random test cases across the input domain. Distance-based ART (D-ART) and Restriction-

    based ART (R-ART) are the first two ART methods, which have considerably

    improved the fault-detection capability of RT. Both these methods, however, require additional

    computation to ensure the generation of evenly spread test cases. To reduce the

    overhead in test case generation, we present in this paper a new ART method using the

    notion of iterative partitioning. The input domain is divided into equally sized cells by a

    grid. The grid cells are categorized into three different groups according to their relative

    locations to successful test cases. In this way, our method can easily identify those grid

    cells that are far apart from all successful test cases for test case generation. Our method

    significantly reduces the time complexity, while keeping the high fault-detection capability.

Authors


Publication Date


  • 2011

Citation


  • Chen, T., Huang, D. Hao. & Zhou, Z. Quan. (2011). On adaptive random testing through iterative partitioning. Journal of Information Science and Engineering, 27 (4), 1449-1472.

Scopus Eid


  • 2-s2.0-80051540419

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1528

Has Global Citation Frequency


Number Of Pages


  • 23

Start Page


  • 1449

End Page


  • 1472

Volume


  • 27

Issue


  • 4

Abstract


  • Random Testing (RT) is an important and fundamental approach to testing computer

    software. Adaptive Random Testing (ART) has been proposed to improve the faultdetection

    capability of RT. ART employs the location information of successful test

    cases (those that have been executed but not revealed a failure) to enforce an even spread

    of random test cases across the input domain. Distance-based ART (D-ART) and Restriction-

    based ART (R-ART) are the first two ART methods, which have considerably

    improved the fault-detection capability of RT. Both these methods, however, require additional

    computation to ensure the generation of evenly spread test cases. To reduce the

    overhead in test case generation, we present in this paper a new ART method using the

    notion of iterative partitioning. The input domain is divided into equally sized cells by a

    grid. The grid cells are categorized into three different groups according to their relative

    locations to successful test cases. In this way, our method can easily identify those grid

    cells that are far apart from all successful test cases for test case generation. Our method

    significantly reduces the time complexity, while keeping the high fault-detection capability.

Authors


Publication Date


  • 2011

Citation


  • Chen, T., Huang, D. Hao. & Zhou, Z. Quan. (2011). On adaptive random testing through iterative partitioning. Journal of Information Science and Engineering, 27 (4), 1449-1472.

Scopus Eid


  • 2-s2.0-80051540419

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1528

Has Global Citation Frequency


Number Of Pages


  • 23

Start Page


  • 1449

End Page


  • 1472

Volume


  • 27

Issue


  • 4