Document Type



Master of Science (MS)


Computer Science

First Advisor's Name

Peter J. Clarke

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Tariq M. King

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Leonardo Bobadilla

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Monique Ross

Fourth Advisor's Committee Title

Committee Member


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

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.



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