Adaptive Random Testing with Coverage Information for Object Oriented Program
Adaptive random testing is a method of test case selection in software testing that is proposed to improve the effectiveness of Random Testing (RT). Since the ART applies distance calculation between test cases, methods for those calculation are needed to be investigated as well. Originally,
the ART is proposed to test the program with numerical output. Some research have been conducted to apply the ART into program with non-numerical output, but they only implemented it into procedural program. In this research, the effectiveness of ART is investigated when being applied into
object oriented software. The distances between test cases are calculated by using coverage information of executed test cases. The type of coverage information being used are decision and method coverage. Applying feedback information has empirically improved the effectiveness of test case
selection process. There are two applied algorithms to calculate the distance between test cases, namely Manhattan Distance and Jaccard Distance. The results of the conducted experiments show that the ART with coverage information improve the effectiveness of RT when applied into object oriented
programs. The effectiveness measurement being used is the F-measure, the number of test cases being executed to detect the first failure. The F-measure of ART methods are less than RT and they are significantly different.
Keywords: Adaptive Random Testing; Coverage Information; Decision Coverage; F-Measure; Jaccard Distance; Manhattan Distance; Method Coverage
Document Type: Research Article
Affiliations: Faculty of Informatics and Electrical Engineering, Del Institute of Technology, Indonesia
Publication date: 01 May 2017
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