2 citations · 2 across the 3 of their papers we have counts for
3 papers
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.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…