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.
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.
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