Abstract

"For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. This dissertation addresses these challenging problems through the use of two complimentary evolutionary computing based systems. The first one is the Fitness Guided Fault Localization (FGFL) system, which novelly uses a specification based fitness function to perform fault localization. The second is the Coevolutionary Automated Software Correction (CASC) system, which employs a variety of evolutionary computing techniques to perform testing, correction, and verification of software. In support of the real world application of these systems, a practitioner's guide to fitness function design is provided. For the FGFL system, experimental results are presented that demonstrate the applicability of fitness guided fault localization to automate this important phase of software correction in general, and the potential of the FGFL system in particular. For the fitness function design guide, the performance of a guide generated fitness function is compared to that of an expert designed fitness function demonstrating the competitiveness of the guide generated fitness function. For the CASC system, results are presented that demonstrate the system's abilities on a series of problems of both increasing size as well as number of bugs present. The system presented solutions more than 90% of the time for versions of the programs containing one or two bugs. Additionally, scalability results are presented for the CASC system that indicate that success rate linearly decreases with problem size and that the estimated convergence rate scales at worst linearly with problem size"--Abstract, page iii

Advisor(s)

Tauritz, Daniel R.

Committee Member(s)

McMillin, Bruce M.
Weigert, Thomas
Sedigh, Sahra
Hurson, A. R.

Department(s)

Computer Science

Degree Name

Ph. D. in Computer Science

Sponsor(s)

Missouri University of Science and Technology. Intelligent Systems Center

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2012

Pagination

xi, 149 pages

Note about bibliography

Includes bibliographical references (pages 142-148).

Rights

© 2012 Joshua Lee Wilkerson, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Automatic control -- Computer programs
Computer software -- Verification
Evolutionary computation
Fault location (Engineering)
Genetic programming (Computer science)
Program transformation (Computer programming)

Thesis Number

T 10039

Print OCLC #

815768170

Electronic OCLC #

801099725

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