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.

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.

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.

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.

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.

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

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.

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.

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

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

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.

TurinTech Co-Founder and CEO Dr. Leslie Kanthan recently joined the Legislate Podcast  to discuss all

Artificial intelligence and machine learning have long been acknowledged by business leaders as a means