An Approach to Generating Test Data for EFSM Paths Considering Condition Coverage | Zendy

Gongzheng Lu | Zendy; Huaikou Miao | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

An Approach to Generating Test Data for EFSM Paths Considering Condition Coverage

Author(s) -

Gongzheng Lu,

Huaikou Miao

Publication year - 2014

Publication title -

electronic notes in theoretical computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.242

H-Index - 60

ISSN - 1571-0661

DOI - 10.1016/j.entcs.2014.12.003

Subject(s) - extended finite state machine , computer science , test data , finite state machine , test case , algorithm , theoretical computer science , data mining , machine learning , programming language , regression analysis

Model-based test case generation has become a hotspot, and automatic generation of test data is difficult in this area. In this paper, system model is represented by extended finite state machine(EFSM), and genetic algorithm is used to generate test data for EFSM paths. When computing the fitness of an individual, the branch distance and the ratio of uncovered conditions of the individual are considered. In experiments, the proposed method is compared with the Kalaji's, and the results show that our method has a better effect and can get higher quality test data

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research