Abstract
Boundary value testing in the white-box setting tests relational expressions with boundary values. These relational expressions are often a part of larger conditional expressions or decisions. It is therefore important, for effective testing that the outcome of a relational expression independently influences the outcome of the expression or decision in which it is embedded. Extending MC/DC to boundary value testing was proposed in the literature as a technique to achieve this independence. Based on this idea, in this paper we formally define a new coverage criterion - masking boundary value coverage (MBVC). MBVC is an adaptation of masking of conditions to boundary value testing. Mutation based analysis is used to show that test data satisfying MBVC is more effective in detecting relational mutants than test data satisfying BVC.
In this paper, we give a formal argument justifying why test data for MBVC is more effective compared to that for BVC in detecting relational mutants. We performed an experiment to evaluate effectiveness and efficiency of MBVC test data relative to that for BVC, in detecting relational mutants. Firstly, mutation adequacy of the test set for MBVC was higher than that for BVC in 56% of cases, and never lower. Secondly, the test data for MBVC killed 80.7% of the total number of mutants generated, whereas the test data for BVC killed only 70.3% of them. A further refined analysis revealed that some mutants are such that they cannot be killed. We selected a small set of mutants randomly to get an estimate of percentage of such mutants. Then the extrapolated mutation adequacies were 92.75% and 80.8% respectively. We summarize the effect of masking on efficiency. Details of the experiment, tools developed for automation and analysis of the results are also provided in this paper.
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Suman, P.V., Muske, T., Bokil, P., Shrotri, U., Venkatesh, R. (2010). Masking Boundary Value Coverage: Effectiveness and Efficiency. In: Bottaci, L., Fraser, G. (eds) Testing – Practice and Research Techniques. TAIC PART 2010. Lecture Notes in Computer Science, vol 6303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15585-7_4
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DOI: https://doi.org/10.1007/978-3-642-15585-7_4
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