Created by W.Langdon from gp-bibliography.bib Revision:1.7325
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.",
ISNI: 0000 0004 7225 1378",
Genetic Programming entries for Fan Wu