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Code‐aware combinatorial interaction testing
Author(s) -
Ahmed Bestoun S.,
Gargantini Angelo,
Zamli Kamal Z.,
Yilmaz Cemal,
Bures Miroslav,
Szeles Marek
Publication year - 2019
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2018.5315
Subject(s) - computer science , white box testing , code coverage , software testing , test management approach , regression testing , keyword driven testing , code (set theory) , manual testing , reliability engineering , software , test case , non regression testing , model based testing , orthogonal array testing , software engineering , programming language , software development , software construction , machine learning , engineering , regression analysis , set (abstract data type)
Combinatorial interaction testing (CIT) is a useful testing technique to address the interaction of input parameters in software systems. CIT has been used as a systematic technique to sample the enormous test possibilities. Most of the research activities focused on the generation of CIT test suites as a computationally complex problem. Less effort has been paid for the application of CIT. To apply CIT, practitioners must identify the input parameters for the Software‐under‐test (SUT), feed these parameters to the CIT test generation tool, and then run those tests on the application with some pass and fail criteria for verification. Using this approach, CIT is used as a black‐box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper introduces a different approach to use the CIT as a gray‐box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. The case studies results showed that this approach would help to detect new faults as compared to the equal impact parameter approach.

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