Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
Effective methods for software testing
Effective methods for software testing
Art of Software Testing
An Empirical Analysis of Equivalence Partitioning, Boundary Value Analysis and Random Testing
METRICS '97 Proceedings of the 4th International Symposium on Software Metrics
A Practical Tutorial on Modified Condition/Decision Coverage
A Practical Tutorial on Modified Condition/Decision Coverage
Using Mutation Analysis for Assessing and Comparing Testing Coverage Criteria
IEEE Transactions on Software Engineering
Weak Mutation Testing and Completeness of Test Sets
IEEE Transactions on Software Engineering
International Journal on Software Tools for Technology Transfer (STTT)
Sufficient mutation operators for measuring test effectiveness
Proceedings of the 30th international conference on Software engineering
The influence of size and coverage on test suite effectiveness
Proceedings of the eighteenth international symposium on Software testing and analysis
Automatic Test Data Generation for C Programs
SSIRI '09 Proceedings of the 2009 Third IEEE International Conference on Secure Software Integration and Reliability Improvement
Test-case generation for embedded simulink via formal concept analysis
Proceedings of the 48th Design Automation Conference
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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.