Pidgin Crasher: Searching for Minimised Crashing GUI Event Sequences

Categories
Artificial Intelligece, Code Optimisation, Research

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.

SBSelector: Search Based Component Selection for Budget Hardware

Categories
Artificial Intelligece, Code Optimisation, Research

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.

Deep Parameter Optimisation

Categories
Artificial Intelligece, Code Optimisation, Research

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.