Genetic Algorithms

Research

ESEC/FSE 2018

Darwinian data structure selection

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.


Michail Basios , Lingbo Li , Fan Wu , Leslie Kanthan , Earl T. Barr, 2018


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Research

Machine Learning

IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning

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.


Yuxi Huan, Fan Wu, Michail Basios, Leslie Kanthan, Lingbo Li, Baowen Xu, 2020


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Research

UCL Discovery

Genetic Optimisation of C++ Applications

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.


Wu, F, 2017


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Research

Search Based Software Engineering

Optimising Darwinian Data Structures on Google Guava

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.


Michail Basios, Lingbo Li, Fan Wu, Leslie Kanthan, Earl T. Barr, 2017


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Research

UCL Discovery

Mutation-based genetic improvement of software

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.


Wu, F, 2017


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Research

Mutation-aware fault prediction

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.


David Bowes, Tracy Hall, Mark Harman, Yue Jia, Federica Sarro, Fan Wu, 2016


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Research

SSBSE

HOMI: Searching Higher Order Mutants for Software Improvement

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.


Fan Wu, Mark Harman, Yue Jia, Jens Krinke, 2016


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ARTICLE

SEAMS

Genetic improvement for adaptive software engineering (keynote)

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.


Mark Harman, Yue Jia, William B. Langdon, Justyna Petke, Iman Hemati Moghadam, Shin Yoo, Fan Wu, 2014


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RESEARCH

Mutation Testing of Memory-Related Operators

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.


Jay Nanavati, Fan Wu, Mark Harman, Yue Jia, Jens Krinke, 2015


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Research

IEEE

The Value of Exact Analysis in Requirements Selection

A decision support framework (METRO) was proposed that handles the Next Release Problem (NRP) by managing better algorithmic and requirements uncertainty.


Lingbo Li; Mark Harman; Fan Wu; Yuanyuan Zhang, 2012


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CONFERENCE PAPER

IEEE

Exact Analysis for Next Release Problem

A new decision support framework for analysing uncertainty in requirements selection and optimisation problem.


Lingbo Li, 2016


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RESEARCH

GECCO

Robust next release problem: handling uncertainty during optimization

A new more efficient genetic based multiobjective algorithm is proposed to optimise software requirements for next release.


Lingbo Li, Mark Harman, Emmanuel Letier, Yuanyuan Zhang, 2014


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