Combinatorial and MC/DC Coverage Levels of Random Testing

Vilkomir, Sergiy; Alluri, Aparna; Kuhn, D. Richard; Kacker, Raghu N.

doi:10.1109/qrs-c.2017.19

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…This technique measures the quality of test cases based on a scenario-based coverage criterion. Recently, Vilkomir et al [16] performed measurements for combinatorial coverage, three cryptographic algorithms are the object of this analysis and the CCM tool is used to measure the combinatorial coverage of random test cases. Since there is a lack of empirical studies measuring combinatorial coverage of manual test cases designed by experienced engineers, we are motivated to investigate this subject in more detail.…”

Section: A Related Work and Backgroundmentioning

confidence: 99%

On Measuring Combinatorial Coverage of Manually Created Test Cases for Industrial Software

Fifo

Enoiu

Afzal

2019

2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Combinatorial coverage has been proposed as a way to measure the quality of test cases by using the input interaction characteristics. This paper describes the results of empirically measuring combinatorial coverage of manually created test cases by experienced industrial engineers working with embedded software development. We found that manual test cases achieve on average 78% 2-way combinatorial coverage, 57% 3-way coverage, 40% 4-way coverage, 20% 5-way combinatorial coverage and 13% for 6-way combinatorial coverage. These manual test cases can be augmented to achieve 100% combinatorial coverage for 2-way and 3-way interactions by adding eight and 66 missing test cases on average, respectively. For 4-way interactions, full combinatorial coverage can achieved by adding 658 missing test cases. For 5-way and 6-way interactions, full combinatorial coverage can be achieved by adding 5163 and 6170 missing test cases on average, respectively. The results of this paper suggest that manual test cases created by industrial engineers do not achieve a high combinatorial coverage and can be improved by adding more test cases to cover t-wise interactions at the expense of more test cases to execute.

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Section: A Related Work and Backgroundmentioning

confidence: 99%

On Measuring Combinatorial Coverage of Manually Created Test Cases for Industrial Software

Fifo

Enoiu

Afzal

2019

2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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“…Vilkomir et al [21] investigated the coverage achieved by randomly generated test cases for two criteria: MC/DC and combinatorial T-way testing, where T-value varies from 2 to 6. They reported that as the number of test cases increases, the coverage level for both T-way and MC/DC increases.…”

Section: Literature Surveymentioning

confidence: 99%

Investigation into the effectiveness of white‐box T‐way testing

2019

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An unduly large number of test cases are required for effective testing of programmes containing complex decision statements. In this context, modified condition/decision coverage (MC/DC) testing has been acknowledged to provide effective testing using a test suite whose size is linear in the number of the clauses present in a predicate. MC/DC testing is wellaccepted and is mandated by several testing standards. T-way testing is another prominent testing technique that helps to limit the combinatorial explosion of black-box test cases. Its application to white-box testing promises to provide effective testing with a comparatively small number of test cases. The authors empirically investigate the effectiveness of MC/DC testing vis-à-vis white-box pairwise, 3-way and 4-way testing.

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