Generating Feasible Path Between Path Testing and Data Flow Testing

Autor:	Ajina A, C. P. Indumathi
Rok vydání:	2020
Předmět:	Computer science business.industry Test data generation Cyclomatic complexity computer.software_genre Data set Software Path (graph theory) Genetic algorithm Control flow graph Data mining business computer Test data
Zdroj:	Evolutionary Computing and Mobile Sustainable Networks ISBN: 9789811552571
DOI:	10.1007/978-981-15-5258-8_32
Popis:	Software testing plays a major role in developing error-free software. The scope of testing the software is to identify the errors and errors present in the software. The data for testing are generated at the initial stage of software testing, which is a complex task during the testing process. There are several techniques that are available to generate test data. The paper puts forth the method to produce the cases that are used in testing from the control flow graph which is based on the path-oriented approach. This technique is to determine the feasible path upon all the possible paths. To solve this technique efficiently, a genetic algorithm is applied to identify the path that is optimal. The results from the pathwise approach are compared to the data flow testing approach. The comparative result shows that the produced data set for path testing produces a more feasible path than the data flow testing technique.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fb6c525ca94ef306ccfeb1dcd76f314c https://doi.org/10.1007/978-981-15-5258-8_32 Zobrazit plný text záznamu