DocumentCode
1617419
Title
A new method of test data generation for branch coverage in software testing based on EPDG and Genetic Algorithm
Author
Chen, Ciyong ; Xu, Xiaofeng ; Chen, Yan ; Li, Xiaochao ; Guo, Donghui
Author_Institution
Dept. of Phys., Xiamen Univ., Xiamen, China
fYear
2009
Firstpage
307
Lastpage
310
Abstract
A new method called EPDG-GA which utilizes the edge partitions dominator graph (EPDG) and genetic algorithm (GA) for branch coverage testing is presented in this paper. First, a set of critical branches (CBs) are obtained by analyzing the EPDG of the tested program, while covering all the CBs implies covering all the branches of the control flow graph (CFG). Then, the fitness functions are instrumented in the right position by analyzing the pre-dominator tree (PreDT), and two metrics are developed to prioritize the CBs. Coverage-Table is established to record the CBs information and keeps track of whether a branch is executed or not. GA is used to generate test data to cover CBs so as to cover all the branches. The comparison results show that this approach is more efficient than random testing approach.
Keywords
flow graphs; genetic algorithms; graph theory; program testing; branch coverage software testing; control flow graph; critical branch; edge partitions dominator graph; fitness function; genetic algorithm; pre-dominator tree; test data generation method; Automatic testing; Costs; Flow graphs; Genetic algorithms; Information science; Instruments; Partitioning algorithms; Physics; Software testing; Tree graphs; Branch Coverage; Edge Partitions; Genetic Algorithm; Test Data Generation;
fLanguage
English
Publisher
ieee
Conference_Titel
Anti-counterfeiting, Security, and Identification in Communication, 2009. ASID 2009. 3rd International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-3883-9
Electronic_ISBN
978-1-4244-3884-6
Type
conf
DOI
10.1109/ICASID.2009.5276897
Filename
5276897
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