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Enhancing Search-based Testing with Testability Transformations for Existing APIs

delete2021-09-28
delete23
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OA
AI
A
Andrea Arcuri *
J
Juan Pablo Galeotti
DOI:10.1145/3477271delete
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摘要

摘要

En 中文
Search-based software testing (SBST) has been shown to be an effective technique to generate test cases automatically. Its effectiveness strongly depends on the guidance of the fitness function. Unfortunately, a common issue in SBST is the so-called flag problem, where the fitness landscape presents a plateau that provides no guidance to the search. In this article, we provide a series of novel testability transformations aimed at providing guidance in the context of commonly used API calls (e.g., strings that need to be converted into valid date/time objects). We also provide specific transformations aimed at helping the testing of REST Web Services. We implemented our novel techniques as an extension to EvoMaster, an SBST tool that generates system-level test cases. Experiments on nine open-source REST web services, as well as an industrial web service, show that our novel techniques improve performance significantly.
Keyword:
SBST
test generation
testability transformation
system testing
REST

期刊

A
ACM Transactions on Software Engineering and Methodology
IF:
6.2
论文数:
1.2K
被引数:
3.4K

机构

U
University of Buenos Aires
学者数:
2.2W
论文数: 1.3W
被引数: 14
K
kristiania university college
学者数:
286
论文数: 354
被引数: 2
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