WorkDifferentWithAI.com Academic Paper Alert!
Written by Chetan Arora, John Grundy, Mohamed Abdelrazek
Category: “AI for IT”
Article Section: AI Development and Operations; AI-Assisted Programming
Publication Date: 2023-11-01
SEO Description: “Examining large language models’ (LLMs) potential in enhancing requirements engineering in software development processes.”
AI-Generated Paper Summary
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The paper “Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs” by Chetan Arora, John Grundy, and Mohamed Abdelrazek explores the potential and application of large-language models (LLMs) in improving the efficiency and accuracy of requirements engineering processes. Requirements Engineering (RE), a critical phase in software development, involves various tasks like elicitation, analysis, specification, and validation of software requirements. These tasks are often complicated by factors such as intricate communication needs, uncertainty during early stages, and lack of automation support. The authors highlight LLMs as promising tools in optimizing tasks related to RE. They propose key directions for research and development in applying LLMs to RE and present preliminary evaluation results to demonstrate this potential.
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Here is an analysis of the key points and potential applications from the paper:
Novelty:
- Proposes using large language models (LLMs) like ChatGPT to automate and enhance requirements engineering (RE) processes. This is a relatively new and untapped application area for LLMs.
- Provides one of the first comprehensive analyses of the strengths, weaknesses, opportunities and threats of applying LLMs to RE stages including elicitation, specification, analysis, and validation.
- Conducts a preliminary evaluation of using ChatGPT for requirements elicitation on a real-world system, highlighting the importance of prompt engineering and the user’s RE experience level.
Potential Commercial Applications:
- LLMs could be incorporated into RE tools and processes in software companies to improve efficiency and quality. For example:
- Auto-generating structured requirements documents from stakeholder inputs.
- Automated quality checks to identify ambiguities, inconsistencies in requirements.
- Assisting with analysis tasks like risk identification, conflict resolution.
- Generating test cases and acceptance criteria.
- LLMs fine-tuned on company/industry data could provide customized assistance for domain-specific projects.
- LLM agents could facilitate real-time collaboration and negotiation between different stakeholders during RE.
- LLM-based “virtual assistants” could be developed to interact with clients/users in natural language to elicit requirements.
- Consulting firms could offer LLM-based RE services for clients lacking expertise.
- Training programs could be developed around best practices for integrating LLMs into RE, especially prompt engineering.
- RE tools companies could integrate LLM capabilities into their products like requirements management platforms.
Overall, the paper highlights the promising potential of LLMs to transform RE but also notes the need for prompt engineering expertise and human oversight. With further research into mitigating limitations, LLMs could enable faster, higher quality RE across industries.
Keywords
Requirements Engineering, Generative AI, Large-Language Models, Software Development, Validation Process
Author’s Abstract
Requirements Engineering (RE) is a critical phase in software development including the elicitation, analysis, specification, and validation of software requirements. Despite the importance of RE, it remains a challenging process due to the complexities of communication, uncertainty in the early stages and inadequate automation support. In recent years, large-language models (LLMs) have shown significant promise in diverse domains, including natural language processing, code generation, and program understanding. This chapter explores the potential of LLMs in driving RE processes, aiming to improve the efficiency and accuracy of requirements-related tasks. We propose key directions and SWOT analysis for research and development in using LLMs for RE, focusing on the potential for requirements elicitation, analysis, specification, and validation. We further present the results from a preliminary evaluation, in this context.