9 citations · 17 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…
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…
Morphy: A Datamorphic Software Test Automation Tool
Hong Zhu, Ian Bayley, Dongmei Liu +1
This paper presents an automated tool called Morphy for datamorphic testing. It classifies software test artefacts into test entities and test morphisms, which are mappings on test…
Datamorphic Testing: A Methodology for Testing AI Applications
Hong Zhu, Dongmei Liu, Ian Bayley +2
With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their qualit…