Grammar-Based Test Generation: new tools and techniques




Wang, Hong-Yi

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Automated testing is superior to manual testing because it is both faster to execute and achieves greater test coverage. Typical test generators are implemented in a programming language of the tester’s choice. Because most programming languages have complex syntax and semantics, the test generators are often difficult to develop and maintain. Context-free grammars are much simpler: they can describe complex test inputs in just a few lines of code. Therefore, Grammar-Based Test Generation (GBTG) has received considerable attention over the years. However, questions about certain aspects of GBTG still remain, preventing its wider application. This thesis addresses these questions using YouGen NG, an experimental framework that incorporates some of the most useful extra-grammatical features found in the GBTG literature. In particular, the thesis describes the mechanisms for (1) eliminating the combinations of less importance generated by a grammar, (2) creating a grammar that generates combinations of correct and error values, (3) generating GUI playback scripts through GBTG, (4) visualizing the language generation process in a complex grammar, and (5) applying GBTG to testing an Really Simple Syndication (RSS) feed parser and a web application called Code Activator (CA).



automated, manual, generators, grammar