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Title: Mutation-based genetic improvement of software
Author: Wu, F.
ISNI:       0000 0004 7225 1378
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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Genetic Improvement (GI) of software is a recent field that has drawn much attention from Software Engineering researchers. It aims to use search techniques to automatically modify and improve existing software. The drawback in previous GI approaches is scalability of these approaches, due to the large search space formed by the code base in real-world systems. To overcome the scalability challenge, more recent studies have confined the granularity of code modification at the statement level and applied a prior sensitivity analysis to further reduce the search space. However, some software improvements may require code changes at a finer level of granularity. This thesis demonstrates that, by combining with Mutation Testing techniques, GI can operate at this finer granularity while preserving scalability. The thesis applies Mutation Operators to automatically modify the source code of the target software. After a prior sensitivity analysis on First Order Mutants, "deep" (previously unavailable) parameters are exposed from the most sensitive locations, followed by a bi-objective optimisation process to fine tune them together with existing ("shallow") parameters. The objective is to improve both time and memory resources required by the computation. Since this approach relies on the selection of Mutation Operators and traditional Mutation Operators are not concerned with memory performance, the thesis proposes and evaluates Memory Mutation Operators in the Mutation Testing context. Using both traditional and Memory Mutation Operators, the thesis further seeks to improve the target software by searching for Higher Order Mutants (HOMs). The thesis presents the result of a code analysis study, which reveals that, among all the code modifications that contribute to the improvement, more than half of them require a finer control of the code, which our approach is better at than previous GI approaches.
Supervisor: Harman, M. ; Krinke, J. ; Jia, Y. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available