Generative AI for Requirements Engineering: A Systematic Literature Review

• URL: http://arxiv.org/abs/2409.06741v1
• Date: Tue, 10 Sep 2024 02:44:39 GMT
• Title: Generative AI for Requirements Engineering: A Systematic Literature Review
• Authors: Haowei Cheng, Jati H. Husen, Sien Reeve Peralta, Bowen Jiang, Nobukazu Yoshioka, Naoyasu Ubayashi, Hironori Washizaki,
• Abstract summary: Generative AI (GenAI) has emerged as a transformative tool in software engineering. This paper analyzes state-of-the-art applications and innovative proposals leveraging GenAI in requirements engineering.
• Score: 4.056534128718579
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: Generative AI (GenAI) has emerged as a transformative tool in software engineering, with requirements engineering (RE) actively exploring its potential to revolutionize processes and outcomes. The integration of GenAI into RE presents both promising opportunities and significant challenges that necessitate systematic analysis and evaluation. Objective: This paper presents a comprehensive systematic literature review (SLR) analyzing state-of-the-art applications and innovative proposals leveraging GenAI in RE. It surveys studies focusing on the utilization of GenAI to enhance RE processes while identifying key challenges and opportunities in this rapidly evolving field. Method: A rigorous SLR methodology was used to analyze 27 carefully selected primary studies in-depth. The review examined research questions pertaining to the application of GenAI across various RE phases, the models and techniques used, and the challenges encountered in implementation and adoption. Results: The most salient findings include i) a predominant focus on the early stages of RE, particularly the elicitation and analysis of requirements, indicating potential for expansion into later phases; ii) the dominance of large language models, especially the GPT series, highlighting the need for diverse AI approaches; and iii) persistent challenges in domain-specific applications and the interpretability of AI-generated outputs, underscoring areas requiring further research and development. Conclusions: The results highlight the critical need for comprehensive evaluation frameworks, improved human-AI collaboration models, and thorough consideration of ethical implications in GenAI-assisted RE. Future research should prioritize extending GenAI applications across the entire RE lifecycle, enhancing domain-specific capabilities, and developing strategies for responsible AI integration in RE practices.

Related papers

• Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey [49.29751866761522]
  This paper aims to investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field. Then, an overview of the latest GenAI models is systematically reviewed. Finally, the corresponding applications in SAR domain are also included.
  arXiv  Detail & Related papers  (2024-11-05T03:06:00Z)
• AI for ERW Detection in Clearance Operations -- The State of Research [12.278116747610158]
  This article provides a literature review of academic research on AI for ERW detection for clearance operations. It finds that research can be grouped into two main streams, AI for ERW object detection and AI for ERW risk prediction. We develop three opportunities for future research, including a call for renewed efforts in the use of AI for ERW risk prediction.
  arXiv  Detail & Related papers  (2024-10-31T11:50:29Z)
• Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report [12.204470166456561]
  Generative AI shows significant potential in health economics and outcomes research (HEOR) Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration.
  arXiv  Detail & Related papers  (2024-10-26T15:42:50Z)
• A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions [0.0]
  RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs. Recent research breakthroughs are discussed, highlighting novel methods for improving retrieval efficiency. Future research directions are proposed, focusing on improving the robustness of RAG models.
  arXiv  Detail & Related papers  (2024-10-03T22:29:47Z)
• Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies [0.0]
  This study examines the impact of Generative Artificial Intelligence (GenAI) on academic research, focusing on its application to qualitative and quantitative data analysis. GenAI tools evolve rapidly, they offer new possibilities for enhancing research productivity and democratising complex analytical processes. Their integration into academic practice raises significant questions regarding research integrity and security, authorship, and the changing nature of scholarly work.
  arXiv  Detail & Related papers  (2024-08-13T13:10:03Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• A Survey on Retrieval-Augmented Text Generation for Large Language Models [1.4579344926652844]
  Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements. This paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation. It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies.
  arXiv  Detail & Related papers  (2024-04-17T01:27:42Z)
• Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision [76.4345564864002]
  Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. We propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents. We present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis.
  arXiv  Detail & Related papers  (2024-04-13T02:39:36Z)
• Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms [50.91348344666895]
  Evolutionary Reinforcement Learning (ERL) integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization. This survey offers a comprehensive overview of the diverse research branches in ERL.
  arXiv  Detail & Related papers  (2024-01-22T14:06:37Z)
• A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
  Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
  arXiv  Detail & Related papers  (2023-09-05T05:06:48Z)
• Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities [63.258517066104446]
  Reinforcement learning integrated as a component in the evolutionary algorithm has demonstrated superior performance in recent years. We discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets.
  arXiv  Detail & Related papers  (2023-08-25T15:06:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.