Our proposed code optimisation solution, called Artemis++, tries to optimise inefficient data structures or library interfaces with automatic exploration and transformation of data structures to optimise software performance.
A new more efficient genetic based multiobjective algorithm is proposed to optimise software requirements for next release.
A new decision support framework for analysing uncertainty in requirements selection and optimisation problem.
This paper introduces 9 Memory Mutation Operators targeting common memory faults and two new testing criteria, the Memory Fault Detection and the Control Flow Deviation criteria to augment the traditional strong mutation testing criterion.
This paper presents a brief outline of an approach to online genetic improvement. We illustrate our proposed approach with a ‘dreaming smart device’ example that combines online and offline machine learning and optimisation.
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.
A decision support framework (METRO) was proposed that handles the Next Release Problem (NRP) by managing better algorithmic and requirements uncertainty.
We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them.
Our novel code optimisation approach applied to optimise the performance of the popular Google Guava Library. Winner of the seach based software engineering challenge award.
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.
In this paper, we introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm to accelerate the overall optimisation process of tuning machine learning models in classification problems.
We introduce ARTEMIS, a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned Darwinian Data Structure, then automatically changes an application to use that DDS.