Abstract:
Requirements management involves the systematic organization and monitoring of the needs
and specifications of a software system throughout its development stages. This intricate
process necessitates efficient teamwork, communication, and monitoring. Software
requirements gathering is a critical phase in software development, influencing the success
and quality of the final product. Traditional methods of requirements gathering often rely on
manual processes, leading to challenges such as ambiguity, inconsistency, and incomplete
specifications.
Requirements gathering is a crucial phase in software development, where stakeholders'
needs and expectations are collected and documented. The process is often time-consuming,
prone to errors, and requires significant manual effort.
AI-based tools have emerged to support this process, leveraging natural language processing
(NLP), machine learning (ML), and other techniques to improve efficiency, accuracy, and
effectiveness.
Artificial intelligence (AI) offers the potential to streamline certain facets of requirements
management like traceability, Quantification and impact analysis. By leveraging machine
learning algorithms, it's feasible to discern the connections between requirements and other
elements like test cases, project knowledge and code.
AI can play a significant role throughout software development life-cycle (SDLC) but in this
research I would like to particularly focus on how AI based requirements gathering can help
in better Knowledge management and Portfolio management by enhancing requirements
traceability,standardization, quantification, decision-making, and overall project prediction
performance, which in turn can help an organization achieve Capability maturity model
(CMMI) level 3 and 4.
Keywords: Chatbot, Semantic annotation, NLP, Requirements engineering, CMMI, Project
management, Knowledge management, portfolio management, Lang-chain, Open-AI
