Title page for ETD etd-04292008-102605

Type of Document

Master's Thesis

Author

Makai, Matthew Charles

Author's Email Address

makaimc@gmail.com

URN

etd-04292008-102605

Title

Incorporating Design Knowledge into Genetic Algorithm-based White-Box Software Test Case Generators

Degree

Master of Science

Department

Computer Science

Advisory Committee

Advisor Name	Title
Chen, Ing-Ray	Committee Co-Chair
Kulczycki, Gregory W.	Committee Co-Chair
Frakes, William B.	Committee Member

Keywords

genetic algorithms
evolutionary computation
evolutionary testing
software testing

Date of Defense

2008-04-24

Availability

unrestricted

Abstract

This thesis shows how to incorporate Unified Modeling Language sequence
diagrams into genetic algorithm-based automated test case generators to increase the code
coverage of their resulting test cases. Automated generation of test data through
evolutionary testing was proven feasible in prior research studies. In those previous
investigations, the metrics used for determining the test generation method effectiveness
were the percentages of testing statement and branch code coverage achieved. However,
the code coverage realized in those preceding studies often converged at suboptimal
percentages due to a lack of guidance in conditional statements. This study compares the
coverage percentages of 16 different Java programs when test cases are automatically
generated with and without incorporating associated UML sequence diagrams. It
introduces a tool known as the Evolutionary Test Case Generator, or ETCG, an automatic
test case generator based on genetic algorithms that provides the ability to incorporate
sequence diagrams to direct the heuristic search process and facilitate evolutionary
testing. When the generator uses sequence diagrams, the resulting test cases showed an
average improvement of 21% in branch coverage and 8% in statement coverage over test
cases produced without using sequence diagrams.

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