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). / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/4339 |
| Date | 07 December 2012 |
| Creators | Wang, Hong-Yi |
| Contributors | Hoffman, Daniel M. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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