The use of metaheuristic search technique for generation of test data has been a burgeoning interest for many researchers in recent times. There has been a previous attempt to automate the test data generation process has been limited, having been constrained by the size and complexity of software, and the fundamental facts that in totality test data generation is an undecideable problem. Metaheuristic search technique is high level frameworks, which utilizes heuristic to seek solution for combinational problem at a reasonable computational cost. This paper describes the design and implementation for test data generation for path testing. The data used is not a unique value for each input variable, we assign to each input interval. This entails exploring the search space more efficiently. We use fitness function which gives truthfully the individual quality. In order to achieve this, some operator were employed. The structured system Analysis and Design methodology was used for proper, analysis and design of the system while implementation was done with Matlab with simulink for simulation. The result obtained displayed a threshold value 0.3183 which is the best fit value gotten after several generation. Also, the system was able to show the Genetic Algorithm fitting of the data. The results was compared with the existing system which only gave a linearly pattern of generation of test data.
Nwogbe, O. (2018). A genetic model for Test Data Generation. Afribary. Retrieved from https://tracking.afribary.com/works/a-genetic-model-for-test-data-generation
Nwogbe, Odinakachi "A genetic model for Test Data Generation" Afribary. Afribary, 15 May. 2018, https://tracking.afribary.com/works/a-genetic-model-for-test-data-generation. Accessed 24 Nov. 2024.
Nwogbe, Odinakachi . "A genetic model for Test Data Generation". Afribary, Afribary, 15 May. 2018. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/a-genetic-model-for-test-data-generation >.
Nwogbe, Odinakachi . "A genetic model for Test Data Generation" Afribary (2018). Accessed November 24, 2024. https://tracking.afribary.com/works/a-genetic-model-for-test-data-generation