GDL

Journal from gdlhub / 2021-08-24 11:54:33
Oleh : Rashmi Rekha Sahoo, Mitrabinda Ray, King Saud University
Dibuat : 2021-08-03, dengan 0 file

Keyword : Search based testing, PSO, APSO, Critical path, Branch distance, Improved combined fitness function
Url : http://www.sciencedirect.com/science/article/pii/S1319157818313004
Sumber pengambilan dokumen : Web

Test case generation is a multi objective problem as the goal is to achieve multiple targets. In existing work, emphasis is given to generate test cases to achieve maximum path coverage. For quality testing, coverage of critical path is more important than percentage of code coverage. The objective of this paper is to generate test cases to achieve maximum path coverage with a challenge of covering a critical path, within the available test resources. At the time of automatic test case generation, a path is critical if the probability of covering the path is low. Search based techniques use metaheuristic algorithms for automated test case generator. Fitness function plays an important role in searching techniques. We propose a fitness function, Improved Combined Fitness (ICF) function, using Adaptive Particle Swarm Optimization (APSO), to generate test cases automatically based on path coverage criteria. We have conducted experiments on three well-known case studies and observed that though both Particle Swarm Optimization (PSO) and APSO with the existing fitness functions, branch distance function and branch distance combined with approximation level, give maximum path coverage, sometimes fail to achieve critical path. Our proposed ICF function applied on APSO gives better result in terms of number of path coverage.

Deskripsi Alternatif :

Test case generation is a multi objective problem as the goal is to achieve multiple targets. In existing work, emphasis is given to generate test cases to achieve maximum path coverage. For quality testing, coverage of critical path is more important than percentage of code coverage. The objective of this paper is to generate test cases to achieve maximum path coverage with a challenge of covering a critical path, within the available test resources. At the time of automatic test case generation, a path is critical if the probability of covering the path is low. Search based techniques use metaheuristic algorithms for automated test case generator. Fitness function plays an important role in searching techniques. We propose a fitness function, Improved Combined Fitness (ICF) function, using Adaptive Particle Swarm Optimization (APSO), to generate test cases automatically based on path coverage criteria. We have conducted experiments on three well-known case studies and observed that though both Particle Swarm Optimization (PSO) and APSO with the existing fitness functions, branch distance function and branch distance combined with approximation level, give maximum path coverage, sometimes fail to achieve critical path. Our proposed ICF function applied on APSO gives better result in terms of number of path coverage.

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