Abstract
Test case generation is popular among the researchers and doing this manually is an exhaustive and time taking process. Automation can cut its cost and create an effective test suite that is evaluated for its adequacy over a set of faults. These faults can be created by applying mutagenic rules that have been used appropriately for searching the improved test inputs in search-based approaches. Researchers have advised these approaches combining mutation testing are more effective at test generation. This paper proposes a novel test generation algorithm SGO-MT by adopting social group optimization algorithm (SGO) with the goal to reveal maximum faults in the software. SGO follows the concept of learning the traits of humans in a group. It works in two phases: acquiring phase (learning from society) and improving phase (learning from the teacher) that try to enhance the fitness of each individual. In learning from society, each individual test case is influenced by another while in the latter case, test data are evolved with respect to the fittest test case. SGO-MT stops functioning when it achieves its desired objective ie. detection of maximum possible artificial faults.
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Acknowledgment
The authors would like to acknowledge Ministry of Electronics and Information Technology, Govt. of India for supporting this research under Visvesvaraya Ph.D. Scheme for Electronics and IT.
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Rani, S., Suri, B. (2019). Adopting Social Group Optimization Algorithm Using Mutation Testing for Test Suite Generation: SGO-MT. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_39
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DOI: https://doi.org/10.1007/978-3-030-24305-0_39
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