Genetic Algorithm-Based Approach for Adequate Test Data Generation

Swain, Swagatika; Mohapatra, Durga Prasad

doi:10.1007/978-81-322-1665-0_43

Cited by 4 publications

(2 citation statements)

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“…Search-based test data generation consists of inspecting the input domain of PUT for test data satisfying a selected test data adequacy criterion. For this reason, the focus has been on the use of metaheuristic search and evolutionary algorithms such as hill climbing, simulated annealing, tabu search, and genetic algorithms [1,[11][12][13][14]. Each of these search-based algorithms is strongly dependent on the domain of the problem under consideration because they use heuristics or knowledge related to the problem domain.…”

Section: Improvements In Rtmentioning

confidence: 99%

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Path-oriented random testing through iterative partitioning (IP-PRT)

Nikravan¹,

Parsa²

2019

Turk J Elec Eng & Comp Sci

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Path-oriented random testing aims at generating a uniformly distributed sequence of test data from a program input domain space to traverse a desired execution path of the program. To this aim, this article proposes a new algorithm to refine a program inputs domain space from invalid subdomains not covering the path. The validity of the subdomains is checked by a constraint propagation method against the path constraints (PCs). The proposed algorithm uses a divideand-conquer technique to iteratively split the inputs domain into subdomains and each time refutes those subdomains that are inconsistent with the PCs. The remaining shrunken subdomains provide all possible test data covering the desired path. Obviously, the more accurate the input domain is, the more effective test data will result. Experiments show the proposed method outperformed other related methods on a set of classical benchmark programs.

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Section: Improvements In Rtmentioning

confidence: 99%

“…Automatic test data generation is concerned with the detection of program input data satisfying a given testing criterion. Currently, path coverage is the most applicable criterion for white-box testing [1,2]. To satisfy this criterion, test data should be generated in such a way that each path could be executed at least once.…”

Section: Introductionmentioning

confidence: 99%