文章基本信息

标题：Test Data Generation for Complex Data Types Using Imprecise Model Constraints and Constraint Solving Techniques
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作者：Sarunas Packevicius ; Greta Krivickaite ; Dominykas Barisas 等
期刊名称：Public Policy And Administration
印刷版ISSN：2029-2872
出版年度：2013
卷号：42
期号：2
页码：131-149
DOI：10.5755/j01.itc.42.2.1855
语种：English
出版社：Kaunas University of Technology
摘要：Number of software applications is growing rapidly, as well as their importance and complexity. The need of quality assurance of these applications is increasing. Testing is one of the key processes to ensure the quality of software and object-oriented applications in particular. In order to test large and complex systems, test automation methods are needed, which evaluate whether the software is working properly. The main goal is to improve effectiveness of object-oriented applications testing by creating an automated test data generation method for complex data structures. This paper presents a test data generation method by adhering to software under test static model and its model constraints. The method provides an algorithm that allows generating test data for complex data structures, by analysing software under test model, its constraints and using constraint solving techniques for building corresponding test data objects and their hierarchies. The presented method is exemplified by simple case studies as well as a large I++ protocol implementing web service project.
关键词：software testing;test data generation;quality assurance;model based testing