3 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
LangChoiceBench: Measuring and Explaining Programming-Language Choice in LLMs
Lukas Twist, Twm Stone, Helen Yannakoudakis +1
Large language models (LLMs) have been shown to exhibit strong Python preferences when generating project-level code, but there is currently no systematic way to measure this behav…
SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering
Jingzhi Gong, Ruizhen Gu, Zhiwei Fei +7
Agent skills are increasingly used to configure coding agents for software engineering (SE) tasks, yet current practice treats them as static, hand-crafted assets, or evolved on pa…
A Study of Library Usage in Agent-Authored Pull Requests
Lukas Twist, Jie M. Zhang
Coding agents are becoming increasingly capable of completing end-to-end software engineering workflows that previously required a human developer, including raising pull requests…
Code Summaries as Diagnostic Context for LLM-Based Program Repair
Lukas Twist, Jie M. Zhang
LLMs can generate useful code, but their outputs often contain small implementation-level bugs with large behavioural effects. In this paper, we ask whether natural-language code s…
Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries
Lukas Twist, Mark Harman, Helen Yannakoudakis +1
Large language models (LLMs) now play a central role in code generation, yet they continue to hallucinate, frequently inventing non-existent libraries. Such library hallucinations…
A Study of LLMs' Preferences for Libraries and Programming Languages
Lukas Twist, Mark Harman, Don Syme +4
Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make…