BOOMPizer: Minimization and prioritization of CONCOLIC based boosted MC/DC test cases

Barisal, Swadhin Kumar; Chauhan, Shorya Pratap Singh; Dutta, Arpita; Godboley, Sangharatna; Sahoo, Bibhudatta; Mohapatra, Durga Prasad

doi:10.1016/j.jksuci.2021.12.007

Cited by 11 publications

(1 citation statement)

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“…Shahbaa increased test coverage through test case prioritization techniques [22]. A recent study published by Barisal and Kumar was related to developing methods for maximizing the structural code coverage for MC/DC [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Maximizing Test Coverage for Security Threats Using Optimal Test Data Generation

et al. 2023

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As time continues to advance, the need for robust security threat mitigation has become increasingly vital in software. It is a constant struggle to maximize test coverage through optimal data generation. We conducted explanatory research to maximize test coverage of security requirements as modeled in the structured misuse case description (SMCD). The acceptance test case is designed through the structured misuse case description for mitigation of security threats. Mal activity is designed from SMCD upon which constraints are specified in object constraint language (OCL) in order to minimize human dependency and improve consistency in the optimal test case design. The study compared two state-of-the-art test coverage maximization approaches through optimal test data generation. It was evident through the results that MC/DC generated optimal test data, i.e., n + 1 test conditions in comparison to the decision coverage approach, i.e., 2n test conditions for security threats. Thus, MC/DC resulted in a significantly lower number of test cases yet maximized test coverage of security threats. We, therefore, conclude that MC/DC maximizes test coverage through optimal test data in comparison to decision coverage at the design level for security threat mitigation.

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Section: Literature Reviewmentioning

confidence: 99%