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A Model-Based AI-Driven Test Generation System

By Dionny Santiago


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

Topics: Testing, Automation, Artificial intelligence, Machine learning, Web classification, Test generation, Language, Artificial Intelligence and Robotics, Computer Sciences, Programming Languages and Compilers, Software Engineering
Publisher: FIU Digital Commons
Year: 2018
OAI identifier:

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