In this tutorial, we present Magpie, a powerful tool for both Genetic Improvement researchers and practitioners. Magpie stands at the forefront of software evolution, providing a streamlined approach to model, evolve, and automatically improve software systems. Addressing both functional and non-functional concerns, Magpie offers a user-friendly no-code interface that seamlessly integrates various search processes as well as enabling easy Python code injection for advanced users to further tailor and specialise the improvement process to meet their specific needs. We will provide a concise overview of Magpie’s internals before exploring diverse real-world scenarios.

Aymeric Blot is a Senior Lecturer at the University of Rennes and a member of the IRISA research centre in the joint Inria/IRISA DiverSE team. Building on previous work at University College London on software specialisation and a doctorate from the University of Lille focused on automated algorithm design for multi-objective combinatorial optimisation, their research explores evolving and optimising software using genetic improvement, automated machine learning, algorithm configuration, and evolutionary computation. This includes leading the development and maintenance of the Magpie automated software improvement framework.