2 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Operational Robustness of LLMs on Code Generation
Debalina Ghosh Paul, Hong Zhu, Ian Bayley
It is now common practice in software development for large language models (LLMs) to be used to generate program code. It is desirable to evaluate the robustness of LLMs for this…
MASTEST: A LLM-Based Multi-Agent System For Testing RESTful APIs
Xiaoke Han, Hong Zhu
Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a multi-agent system called MASTEST that com…
Investigating The Smells of LLM Generated Code
Debalina Ghosh Paul, Hong Zhu, Ian Bayley
Context: Large Language Models (LLMs) are increasingly being used to generate program code. Much research has been reported on the functional correctness of generated code, but the…
ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generation
Debalina Ghosh Paul, Hong Zhu, Ian Bayley
In the scenario-based evaluation of machine learning models, a key problem is how to construct test datasets that represent various scenarios. The methodology proposed in this pape…
User Centric Evaluation of Code Generation Tools
Tanha Miah, Hong Zhu
With the rapid advance of machine learning (ML) technology, large language models (LLMs) are increasingly explored as an intelligent tool to generate program code from natural lang…