6 papers
How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study
Yunbo Lyu, David Williams, Jieke Shi +5
The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been m…
Pomona: Continuous Code Quality Improvement via Small, Agentic Pull Requests at Bloomberg
David Williams, Angelos Evripiotis, Serkan Kirbas +4
In this industrial experience paper, we present Pomona, a lightweight agentic tool that utilises agent skills for continuous code quality improvement. Inspired by the Kaizen (TM) p…
SafeTune: Search-based Harmfulness Minimisation for Large Language Models
Giordano d'Aloisio, David Williams, Giusy Annunziata +3
The widespread adoption of Large Language Models (LLMs) raises concerns about the potential harmfulness of their responses. In this paper, we first investigate the harmfulness of r…
BayesInsights: Modelling Software Delivery and Developer Experience with Bayesian Networks at Bloomberg
Serkan Kirbas, Federica Sarro, David Williams
As software in industry grows in size and complexity, so does the volume of engineering data that companies generate and use. Ideally, this data could be used for many purposes, in…
Unveiling Practical Shortcomings of Patch Overfitting Detection Techniques
David Williams, Ioakim Avraam, Aldeida Aleti +3
Automated Program Repair (APR) can reduce the time developers spend debugging, allowing them to focus on other aspects of software development. Automatically generated bug patches…
Empirical and Sustainability Aspects of Software Engineering Research in the Era of Large Language Models: A Reflection
David Williams, Max Hort, Maria Kechagia +3
Software Engineering (SE) research involving the use of Large Language Models (LLMs) has introduced several new challenges related to rigour in benchmarking, contamination, replica…