Mutation Testing Using Predictive Methods
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science and Info Sys
Date of Award
Fall 2021
Abstract
Software quality is a critical part in the software development cycle. Mutation testing is an approach that assesses the effectiveness of software testing. As mutation testing is a computationally expensive process, finding ways to reduce the cost is an important part of extending the benefits of software testing. Machine learning concepts are applied to mutation testing to evaluate the cost reduction potential for mutation testing. Key features that may show the status of mutant are extracted and fed to machine learning models. These models have the potential to cut the cost of mutation testing by predicting which mutants will live without extensive execution of the software testing.
Advisor
Omar El Ariss
Subject Categories
Computer Sciences | Physical Sciences and Mathematics
Recommended Citation
Duckworth, Stephanie Nichole, "Mutation Testing Using Predictive Methods" (2021). Electronic Theses & Dissertations. 527.
https://digitalcommons.tamuc.edu/etd/527
