4 papers
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…
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…
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…
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…