3 citations · 7 across the 12 of their papers we have counts for
9 papers · 1 filter
Requirements-Augmented Generation for Trustworthy Acceptance Testing of LLM-Based Software
Fanyu Wang, Chetan Arora, Zhenping Xie +4
LLM-based software (LBS) integrates large language models as core components to deliver flexible, personalised responses. Unlike traditional software with deterministic outputs, LB…
Class Model Generation from Requirements using Large Language Models
Jackson Nguyen, Rui En Koe, Fanyu Wang +2
The emergence of Large Language Models (LLMs) has opened new opportunities to automate software engineering activities that traditionally require substantial manual effort. Among t…
Reporting LLM Prompting in Automated Software Engineering: A Guideline Based on Current Practices and Expectations
Alexander Korn, Lea Zaruchas, Chetan Arora +4
Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided thro…
Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs
Fanyu Wang, Chetan Arora, Yonghui Liu +5
Acceptance criteria (ACs) play a critical role in software development by clearly defining the conditions under which a software feature satisfies stakeholder expectations. However…
From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry
Chetan Arora, Fanyu Wang, Chakkrit Tantithamthavorn +2
Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constra…
Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap
Kaicheng Huang, Fanyu Wang, Yutan Huang +1
Advancements in large language models (LLMs) have led to a surge of prompt engineering (PE) techniques that can enhance various requirements engineering (RE) tasks. However, curren…
Requirements-Driven Automated Software Testing: A Systematic Review
Fanyu Wang, Chetan Arora, Chakkrit Tantithamthavorn +2
Automated software testing has significant potential to enhance efficiency and reliability within software development processes. However, its broader adoption faces considerable c…
Comparing Human and LLM Generated Code: The Jury is Still Out!
Sherlock A. Licorish, Ansh Bajpai, Chetan Arora +2
Much is promised in relation to AI-supported software development. However, there has been limited evaluation effort in the research domain aimed at validating the true utility of…
Optimizing Large Language Model Hyperparameters for Code Generation
Chetan Arora, Ahnaf Ibn Sayeed, Sherlock Licorish +2
Large Language Models (LLMs), such as GPT models, are increasingly used in software engineering for various tasks, such as code generation, requirements management, and debugging.…