Prioritizing Software Requirements Using Large Language Models
- URL: http://arxiv.org/abs/2405.01564v1
- Date: Fri, 5 Apr 2024 15:20:56 GMT
- Title: Prioritizing Software Requirements Using Large Language Models
- Authors: Malik Abdul Sami, Zeeshan Rasheed, Muhammad Waseem, Zheying Zhang, Tomas Herda, Pekka Abrahamsson,
- Abstract summary: This article focuses on requirements engineering, typically seen as the initial phase of software development.
The challenge of identifying requirements and satisfying all stakeholders within time and budget constraints remains significant.
This study introduces a web-based software tool utilizing AI agents and prompt engineering to automate task prioritization.
- Score: 3.9422957660677476
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) are revolutionizing Software Engineering (SE) by introducing innovative methods for tasks such as collecting requirements, designing software, generating code, and creating test cases, among others. This article focuses on requirements engineering, typically seen as the initial phase of software development that involves multiple system stakeholders. Despite its key role, the challenge of identifying requirements and satisfying all stakeholders within time and budget constraints remains significant. To address the challenges in requirements engineering, this study introduces a web-based software tool utilizing AI agents and prompt engineering to automate task prioritization and apply diverse prioritization techniques, aimed at enhancing project management within the agile framework. This approach seeks to transform the prioritization of agile requirements, tackling the substantial challenge of meeting stakeholder needs within set time and budget limits. Furthermore, the source code of our developed prototype is available on GitHub, allowing for further experimentation and prioritization of requirements, facilitating research and practical application.
Related papers
- Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - Dealing with Data for RE: Mitigating Challenges while using NLP and
Generative AI [2.9189409618561966]
Book chapter explores the evolving landscape of Software Engineering in general, and Requirements Engineering (RE) in particular.
We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems.
Book provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary.
arXiv Detail & Related papers (2024-02-26T19:19:47Z) - Advancing Requirements Engineering through Generative AI: Assessing the
Role of LLMs [10.241642683713467]
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 Requirements Engineering processes, aiming to improve the efficiency and accuracy of requirements-related tasks.
arXiv Detail & Related papers (2023-10-21T11:29:31Z) - Challenges in aligning requirements engineering and verification in a
large-scale industrial context [7.92131557859946]
This paper presents preliminary findings of interviews that identify key challenges in aligning requirements and verification processes.
The findings of this study can be used by practitioners as a basis for investigating alignment in their organizations.
arXiv Detail & Related papers (2023-07-23T20:08:49Z) - Requirements Engineering Framework for Human-centered Artificial
Intelligence Software Systems [9.642259026572175]
We present a new framework developed based on human-centered AI guidelines and a user survey to aid in collecting requirements for human-centered AI-based software.
The framework is applied to a case study to elicit and model requirements for enhancing the quality of 360 degreevideos intended for virtual reality (VR) users.
arXiv Detail & Related papers (2023-03-06T06:37:50Z) - Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering.
In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z) - AI Techniques for Software Requirements Prioritization [91.3755431537592]
The prioritization approaches discussed in this paper are based on different Artificial Intelligence (AI) techniques that can help to improve the overall quality of requirements prioritization processes.
arXiv Detail & Related papers (2021-08-02T12:43:00Z) - Towards Utility-based Prioritization of Requirements in Open Source
Environments [51.65930505153647]
We show how utility-based prioritization approaches can be used to support contributors in conventional and open source Requirements Engineering scenarios.
As an example, we show how dependencies can be taken into account in utility-based prioritization processes.
arXiv Detail & Related papers (2021-02-17T09:05:54Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z) - Technology Readiness Levels for AI & ML [79.22051549519989]
Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results.
We propose a proven systems engineering approach for machine learning development and deployment.
arXiv Detail & Related papers (2020-06-21T17:14:34Z)
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.