Research

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

GECCO '15

Deep Parameter Optimisation

We introduce a mutation-based approach to automatically discover and expose ‘deep’ (previously unavailable) parameters that affect a program’s runtime costs. These discovered parameters, together with existing (‘shallow’) parameters, form a search space that we tune using search-based optimisation in a bi-objective formulation that optimises both time and memory consumption.


Fan Wu, Westley Weimer, Mark Harman, Yue Jia, Jens Krinke, 2015


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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

Machine Learning

Better Model Selection with a new Definition of Feature Importance

In this paper, we propose a new tree-model explanation approach for model selection.


Fan Fang, Carmine Ventre, Lingbo Li, Leslie Kanthan, Fan Wu, Michail Basios, 2020


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Research

Deep Learning, Finance, Trading

Ascertaining price formation in cryptocurrency markets with DeepLearning

In this paper is was shown how deep learning approaches can be used to predict the direction of the mid-price changes of crypto assets.


Fan Fang, Waichung Chung, Carmine Ventre, Michail Basios, Leslie Kanthan, Lingbo Li, Fan Wu, 2020


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Research

Machine Learning, Trading

Ascertaining price formation in cryptocurrency markets with machine learning

In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting.


Fan Fang, Waichung Chung, Carmine Ventre, Michail Basios, Leslie Kanthan, Lingbo Li & Fan Wud Turing, 2021


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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

UCL Discovery

Checkers: Multi-modal darwinian API optimisation

In this paper, we discuss an automated approach for exploring API equivalence and a framework to synthesise semantically equivalent programs.


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

IEEE

Clone Detection on Large Scala Codebases

We conducted large scale experimental research on the performance of two state-of-the-art code clone detection techniques, SourcererCC and AutoenCODE, on both open source projects and an industrial project written in the Scala language.


Wahidur Rahman, Yisen Xu, Fan Pu; Jifeng Xuan; Xiangyang Jia, Michail Basios, Leslie Kanthan, Lingbo Li, Fan Wu, Baowen Xu, 2020


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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

Software Applications (NASAC)

Mining the use of higher-order functions: An exploratory study on Scala programs

In this paper, we investigate the use of higher-order functions in Scala programs.


Yisen Xu, Fan Wu, Xiangyang Jia, Lingbo Li & Jifeng Xuan, 2020


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Research

Trading and Market Microstructure

Cryptocurrency Trading: A Comprehensive Survey

This is one of the most influencial and first surveys in the area of cryptocurrency trading which explains in details how machine learning techniques can be used for algo trading.


Fan Fang, Carmine Ventre, Michail Basios, Leslie Kanthan, Lingbo Li, David Martinez-Regoband, Fan Wu, 2020


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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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Research

Science Direct

Memory mutation testing

We introduce Memory Mutation Testing, proposing 9 Memory Mutation Operators each of which targets common forms of memory fault. We compare Memory Mutation Operators with traditional Mutation Operators, while handling equivalent and duplicate mutants.


Fan Wu, Jay Nanavati, Mark Harman Yue Jia, Jens Krinke, 2017


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