HOMI: Searching Higher Order Mutants for Software Improvement

Categories
Code Optimisation, Genetic Algorithms, Research

This paper introduces a Higher Order Mutation based approach for Genetic Improvement of software, in which the code modification granularity is finer than in previous work while scalability remains. The approach applies the NSGAII algorithm to search for higher order mutants that improve the non-functional properties of a program while passing all its regression tests.

Mutation-based genetic improvement of software

Categories
Code Optimisation, Genetic Algorithms, Research

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.