Innovative Approaches to Impact Analysis of Requirement Changes using LLM in Software Projects

The software industry is continuously evolving because of emerging technologies, evolving development methodologies, changing business needs, and shifting stakeholder preferences. Effectively managing daily changes is critical, especially given the complexity of requirement change management (RCM),...

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Hauptverfasser: Rusdianto, Denny Sagita, Fabroyir, Hadziq, Yuhana, Umi Laili
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung:The software industry is continuously evolving because of emerging technologies, evolving development methodologies, changing business needs, and shifting stakeholder preferences. Effectively managing daily changes is critical, especially given the complexity of requirement change management (RCM), which requires the development of adaptable systems that accurately reflect dynamic conditions. This paper explores the integration of Large Language Models (LLMs), specifically GPT-4, into software engineering to enhance the effectiveness of RCM. This paper proposes a novel methodology that leverages the sophisticated reasoning capabilities of LLM GPT-4 to analyze and predict the impacts of software requirement changes before implementation. Our approach combines LLMs with a structured analysis framework that interprets changes in software requirements and evaluates their potential impacts across multiple dimensions. The use of GPT-4 can revolutionize how software engineers understand and implement requirements changes, providing a comprehensive tool for predicting the impact of such changes and evaluating the consequences accurately. The results of this research can help requirements engineers perform automated change impact analysis, enabling them to make informed decisions regarding the acceptance or rejection of requirement change requests. This ultimately supports the creation of software products that better align with consumer needs and expectations, ensuring enhanced user experiences and satisfaction.
ISSN:2159-1423
DOI:10.1109/ISCT62336.2024.10791169