2 citations · 3 across the 5 of their papers we have counts for
5 papers
Boosting LLMs for Mutation Generation
Bo Wang, Ming Deng, Mingda Chen +5
LLM-based mutation testing is a promising testing technology, but existing approaches typically rely on a fixed set of mutations as few-shot examples or none at all. This can resul…
Towards a more efficient bias detection in financial language models
Firas Hadj Kacem, Ahmed Khanfir, Mike Papadakis
Bias in financial language models constitutes a major obstacle to their adoption in real-world applications. Detecting such bias is challenging, as it requires identifying inputs w…
One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
Amal Akli, Maxime Cordy, Mike Papadakis +1
Large language models have recently surpassed specialized systems on code generation, yet their effectiveness on other code-analysis tasks remains less clear. At the same time, mul…
YATE: The Role of Test Repair in LLM-Based Unit Test Generation
Michael Konstantinou, Renzo Degiovanni, Jie M. Zhang +2
Recent advances in automated test generation utilises language models to produce unit tests. While effective, language models tend to generate many incorrect tests with respect to…
Can we find bugs using LLM-generated oracles?
Michael Konstantinou, Renzo Degiovanni, Mike Papadakis
Unit testing is vital in software development. Typically, a unit test consists of a test prefix and a test oracle which captures the developer's intended behaviour. Traditional tes…