Automatic Generation and Optimization of Combinatorial Test Cases from UML Activity Diagram Using Particle Swarm Optimization

Tatale, Subhash; Prakash, V. Chandra

doi:10.18280/isi.270106

Cited by 3 publications

(3 citation statements)

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“…A recent approach [26] is focused on combinational testing to generate TC. Authors in this study used SD for generating combinatorial test cases.…”

Section: Related Workmentioning

confidence: 99%

A UML-based Approach towards Test Case Generation and Optimization

2024

KSII TIIS

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Software testing is an important phase as it ensures the software quality. The software testing process comprises of three steps: generation, execution, and evaluation of test cases. Literature claims the usage of single and multiple 'Unified Modeling Language' (UML) diagrams to generate test cases. Using multiple UML diagrams increases test case coverage. However, the existing approaches show limitations in test case generation from UML diagrams. Therefore, in this research study, we propose an approach to generate the test cases using UML State Chart Diagram (SCD), Activity Diagram (AD), and Sequence Diagram (SD). The proposed approach transforms UML diagrams into intermediate forms: SCD Graph, AD Graph, and SD Graph respectively. Furthermore, by integrating these three graphs, a System Testing Graph (STG) is formed. Finally, test cases are identified from STG by using a traversal algorithm such as Depth First Search (DFS) that is an optimization method. The results show that the proposed approach is better compared to existing approaches in terms of coverage and performance. Moreover, the generated test cases have the ability to detect faults at the unit level, integration, and system level testing.

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“…A recent approach [26] is focused on combinational testing to generate TC. Authors in this study used SD for generating combinatorial test cases.…”

Section: Related Workmentioning

confidence: 99%

A UML-based Approach towards Test Case Generation and Optimization

2024

KSII TIIS

View full text Add to dashboard Cite

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“…Test coverage maximization has been exercised in a number of ways in the literature. Subhash and Vudatha maximized test coverage through combinatorial test cases using the particle swarm optimization algorithm [17]. Avdeenko increased code coverage through automated test data generation based on a genetic algorithm [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A UML diagram can be used as input for a test case generation technique, and test cases can then be validated using another technique [17]. Moreover, when using UML models we need to ensure that the generated code is correct for the UML Model.…”

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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Maximizing test coverage for security threats Using Optimal Test Data Generation

Hussain¹,

Faiz²,

Aljaidi³

et al. 2023

Preprint

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As time continues to advance, the need for robust security threat mitigation has become increasingly vital in software. However, ensuring early effective security threat mitigation requires optimal test data and consistent test case design. It is a constant struggle to maximize test coverage through test data optimization. We conducted explanatory research to maximize test coverage of security requirements as modeled in Structured Misuse Case Description (SMCD) i.e., structured specification of misuse case, so as to improve consistency in optimal test data generation. We specified constraints upon Mal activity in Object Constraint Language (OCL) in order to minimize human dependency and improve consistency in optimal test data generation. It was evident through results that MC/DC generated optimal test data of security threats through SMCD in comparison to the Decision Coverage method thus resulting in designing a significantly lower number of test cases and yet maximizing test coverage of security threats. MC/DC generated test data with n+1, while Decision Coverage generated test data with〖 2〗^n, we, therefore, conclude that MC/DC maximizes test coverage through optimal test data from SMCD in comparison to Decision Coverage.

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Automatic Generation and Optimization of Combinatorial Test Cases from UML Activity Diagram Using Particle Swarm Optimization

Cited by 3 publications

References 0 publications

A UML-based Approach towards Test Case Generation and Optimization

A UML-based Approach towards Test Case Generation and Optimization

Maximizing Test Coverage for Security Threats Using Optimal Test Data Generation

Maximizing test coverage for security threats Using Optimal Test Data Generation

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