There are countless benefits of incorporating AI into your business, including company efficiency, customisation and

Increasingly more organisations are adopting AI solutions to refine decision-making processes. From understanding consumer sentiment

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

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.

Forecasting is a crucial component in decision-making. In order to make good decisions in the

With digitalisation accelerating, more and more companies are adopting AI to maximise their company value.

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.

A framework that simulates markets and the behaviour of its actors using decision making AI agents.

We present a search based testing system that automatically explores the space of all possible GUI event interleavings. Search guides our system to novel crashing sequences using Levenshtein distance and minimises the resulting fault-revealing UI sequences in a post-processing hill climb.

Determining which functional components should be integrated to a large system is a challenging task, when hardware constraints, such as available memory, are taken into account. We formulate such problem as a multi-objective component selection problem, which searches for feature subsets that balance the provision of maximal functionality at minimal memory resource cost.

“This paper presents a brief outline of a higher-order mutation based framework for Genetic Improvement (GI). We argue that search-based higher-order mutation testing can be used to implement a form of genetic programming (GP) to increase the search granularity and testability of GI.”

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.

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.