Automatic Generation of Specification-Based Test Cases by Applying Genetic Algorithms in Reinforcement Learning

Sato, Yuji; Sugihara, Taku

doi:10.1007/978-3-319-31220-0_5

Cited by 3 publications

(1 citation statement)

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“…Each functional scenario defines an independent function of the operation: when the test condition holds on the input variables, the output variables will be defined by the defining condition. Currently, test data generation from a functional scenario only takes the test condition into account meanwhile leaving the defining condition untouched [9,42,43]. Now, we explain how to make a slight extension to change the form of defining conditions so as to allow bad behaviors to occur.…”

Section: Definition 1 a Fsf Of Process Is The Disjunction Of Functiomentioning

confidence: 99%

Mutated Specification-Based Test Data Generation with a Genetic Algorithm

2021

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Specification-based testing methods generate test data without the knowledge of the structure of the program. However, the quality of these test data are not well ensured to detect bugs when non-functional changes are introduced to the program. To generate test data effectively, we propose a new method that combines formal specifications with the genetic algorithm (GA). In this method, formal specifications are reformed by GA in order to be used to generate input values that can kill as many mutants of the target program as possible. Two classic examples are presented to demonstrate how the method works. The result shows that the proposed method can help effectively generate test cases to kill the program mutants, which contributes to the further maintenance of software.

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Section: Definition 1 a Fsf Of Process Is The Disjunction Of Functiomentioning

confidence: 99%