Master of Science (MS)
First Advisor's Name
Peter J. Clarke
First Advisor's Committee Title
Second Advisor's Name
Tariq M. King
Second Advisor's Committee Title
Third Advisor's Name
Third Advisor's Committee Title
Fourth Advisor's Name
Fourth Advisor's Committee Title
Testing, Automation, Artificial intelligence, Machine learning, Web classification, Test generation, Language
Date of Defense
Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. A total of 250 test flows were also manually hand-crafted for training purposes. Various machine learning algorithms were evaluated. Results showed that Random Forest classifiers performed well on several web component classification problems. In addition, Long Short-Term Memory neural networks were able to model and generate new valid test flows.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Santiago, Dionny, "A Model-Based AI-Driven Test Generation System" (2018). FIU Electronic Theses and Dissertations. 3878.
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