3 citations · 7 across the 12 of their papers we have counts for
13 papers
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…
LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues
Fanyu Wang, Xiaoxi Kang, Paul Burgess +6
More than half of the global population struggles to meet their civil justice needs due to limited legal resources. While Large Language Models (LLMs) have demonstrated impressive…
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…
AxBERT: An Interpretable Chinese Spelling Correction Method Driven by Associative Knowledge Network
Fanyu Wang, Hangyu Zhu, Zhenping Xie
Deep learning has shown promising performance on various machine learning tasks. Nevertheless, the uninterpretability of deep learning models severely restricts the usage domains t…
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.…
An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection
Fanyu Wang, Zhenping Xie
Text correction, especially the semantic correction of more widely used scenes, is strongly required to improve, for the fluency and writing efficiency of the text. An adversarial…
Constructing Word-Context-Coupled Space Aligned with Associative Knowledge Relations for Interpretable Language Modeling
Fanyu Wang, Zhenping Xie
As the foundation of current natural language processing methods, pre-trained language model has achieved excellent performance. However, the black-box structure of the deep neural…