Title

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

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