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
  • genetic algorithms
  • evolutionary computation
  • evolutionary testing
  • software testing
Date of Defense 2008-04-24
Availability unrestricted
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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