7 citations · 14 across the 4 of their papers we have counts for
5 papers
LLMLOOP: Improving LLM-Generated Code and Tests through Automated Iterative Feedback Loops
Ravin Ravi, Dylan Bradshaw, Stefano Ruberto +2
Large Language Models (LLMs) are showing remarkable performance in generating source code, yet the generated code often has issues like compilation errors or incorrect code. Resear…
LLMORPH: Automated Metamorphic Testing of Large Language Models
Steven Cho, Stefano Ruberto, Valerio Terragni
Automated testing is essential for evaluating and improving the reliability of Large Language Models (LLMs), yet the lack of automated oracles for verifying output correctness rema…
Metamorphic Testing of Large Language Models for Natural Language Processing
Steven Cho, Stefano Ruberto, Valerio Terragni
Using large language models (LLMs) to perform natural language processing (NLP) tasks has become increasingly pervasive in recent times. The versatile nature of LLMs makes them app…
Automated Trustworthiness Testing for Machine Learning Classifiers
Steven Cho, Seaton Cousins-Baxter, Stefano Ruberto +1
Machine Learning (ML) has become an integral part of our society, commonly used in critical domains such as finance, healthcare, and transportation. Therefore, it is crucial to eva…
SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
Stefano Ruberto, Valerio Terragni, Jason H. Moore
Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) tha…