A Review of Hot Fixing
The first comprehensive survey of the literature for hot fixing software.
I am a Lecturer/Assistant Professor in the School of Informatics at the University of Edinburgh and a member of the Institute for Computing Systems Architecture (ICSA).
My research interests span a broad range of topics in Software Engineering, with a focus on developing automated, AI-driven techniques to improve both functional and non-functional software properties. I am also interested in evolutionary computation and search-based optimisation, particularly genetic improvement and its integration with AI techniques.
Before joining Edinburgh, I completed my PhD in Computer Science from University College London (UCL), where I was part of the Software Systems Engineering Group under the supervision of Prof. Justyna Petke and Prof. Federica Sarro. My doctoral thesis, “Fixing Under Fire: Practices, Perspectives, and Automation for Hot Fixing Time-Critical Software Issues,” investigated how time-critical software issues are addressed in practice and how automated techniques can support their resolution.
I am an active member of the software engineering research community, serving as a reviewer and organiser for leading conferences and journals. Beyond Academia, I am passionate about the startup ecosystem, with experience in venture capital and early-stage deep tech startups.
I am always looking for motivated students who are passionate about research and building safe, reliable, and sustainable software systems. If you are interested in pursuing a PhD or collaborating on related research topics, please feel free to get in touch!
PhD Computer Science
University College London
MSc Software Systems Engineering
University College London
BSc Computer Science
Technion Institute of Technology
The first comprehensive survey of the literature for hot fixing software.
This study presents a questionnaire conducted with software practitioners to investigate hot fixing practices. An extended version of this work, incorporating in-depth interviews, …
The first hot fix benchmark. A comprehensive benchmark of Java hot fixes that address time-critical bugs.
Reinforcement learning-based approach for the selection of mutation operators in heuristic-based program repair.