activity
20242026
most citedBenchmarks and Metrics for Evaluations of Code Generation: A Critical Review

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SE2026

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…

cs.SE2025

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…

cs.SE2025

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…

cs.CL2024

Exploring Performance Contrasts in TableQA: Step-by-Step Reasoning Boosts Bigger Language Models, Limits Smaller Language Models

Haoyan Yang, Yixuan Wang, Keyue Tong +2

This paper proposes a detailed prompting flow, termed Table-Logic, to investigate the performance contrasts between bigger and smaller language models (LMs) utilizing step-by-step…

cs.AI2024★ 2 cited

Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review

Debalina Ghosh Paul, Hong Zhu, Ian Bayley

With the rapid development of Large Language Models (LLMs), a large number of machine learning models have been developed to assist programming tasks including the generation of pr…

cs.SE2024

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…

cs.SE2024★ 2 cited

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…