RT Journal Article
A1 Shujuan Jiang
A1 Jieqiong Chen
A1 Yanmei Zhang
A1 Junyan Qian
A1 Rongcun Wang
A1 Meng Xue

PB iet
T1 Evolutionary approach to generating test data for data flow test
JN IET Software
VO 12
IS 4
SP 318
OP 323
AB Software testing consumes a significant portion of software effort. Program entities such as branch or definition–use pairs (DUPs) are used in diverse software development tasks. In this study, the authors present a novel evolution-based approach to generating test data for all definition–use coverage. First, the subset of DUPs, which can ensure the coverage adequacy, is computed by a reduction algorithm for the whole DUPs. Then they apply a genetic algorithm to generate test data for the subset of DUPs. Furthermore, the fitness of an individual depends on the matching degree between the traversed path and the definition-clear path of each target DUP. They also investigate the coverage and the size of test cases of test data generation by applying the authors’ approach on 15 widely used subject programs. The experimental results show that their approach can reduce the size of test cases that generated without affecting the coverage rate.
K1 diverse software development tasks
K1 genetic algorithm
K1 software testing
K1 test cases
K1 branch
K1 test data generation
K1 DUPs
K1 authors
K1 software effort
K1 definition–use coverage
K1 evolutionary approach
K1 definition-clear path
K1 data flow test
K1 definition–use pairs
DO https://doi.org/10.1049/iet-sen.2018.5197
UL https://digital-library.theiet.org/;jsessionid=k9c51bhfeq4o.x-iet-live-01content/journals/10.1049/iet-sen.2018.5197
LA English
SN 1751-8806
YR 2018
OL EN