37 citations · 58 across the 6 of their papers we have counts for
6 papers
BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification
Mohammad Amin Zadenoori, Waad Alhoshan, Jacek Dąbrowski +2
Large language models (LLMs) are increasingly applied to requirements engineering (RE) tasks, yet the prompts guiding them are typically designed manually through trial and error,…
Large Language Models (LLMs) for Requirements Engineering (RE): A Systematic Literature Review
Mohammad Amin Zadenoori, Jacek Dąbrowski, Waad Alhoshan +2
Large Language Models (LLMs) are finding applications in numerous domains, and Requirements Engineering (RE) is increasingly benefiting from their capabilities to assist with compl…
How Effective are Generative Large Language Models in Performing Requirements Classification?
Waad Alhoshan, Alessio Ferrari, Liping Zhao
In recent years, transformer-based large language models (LLMs) have revolutionised natural language processing (NLP), with generative models opening new possibilities for tasks th…
Zero-Shot Learning for Requirements Classification: An Exploratory Study
Waad Alhoshan, Alessio Ferrari, Liping Zhao
Context: Requirements engineering researchers have been experimenting with machine learning and deep learning approaches for a range of RE tasks, such as requirements classificatio…
Classification of Natural Language Processing Techniques for Requirements Engineering
Liping Zhao, Waad Alhoshan, Alessio Ferrari +1
Research in applying natural language processing (NLP) techniques to requirements engineering (RE) tasks spans more than 40 years, from initial efforts carried out in the 1980s to…
Natural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study
Liping Zhao, Waad Alhoshan, Alessio Ferrari +4
Natural language processing supported requirements engineering is an area of research and development that seeks to apply NLP techniques, tools and resources to a variety of requir…