Open AccessTest Case Generation for Web Application Based on Markov Reward Process
Author(s) -
Jing Cao,
Xiaoqiang Liu,
Hongchen Guo,
Lizhi Cai,
Yashan Hu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1792/1/012039
Subject(s) - computer science , correctness , process (computing) , markov chain , test case , markov process , code coverage , markov model , test (biology) , software , machine learning , algorithm , programming language , mathematics , paleontology , biology , statistics , regression analysis
Web applications often face continuous updating due to functional change or UI renew, while it remains a challenge to guarantee their correctness. The goal of software testing is to find defects in a limited time range whereas exhaustive testing is an ideal yet time-consuming process. In this research, we propose an approach to generating test cases automatically based on the Markov reward process which innovatively contains a reward function for test results to guide the generation of test cases. By using the N-step algorithm, this approach can generate the test flow with the highest risk priority which can capture software defects as quickly as possible. The experiment on an e-commerce system shows that there is significant improvement on the defect detection capability of test cases generated through Markov reward process.