Software Engineering Team using Large Language Models

Computer Science

The rapid development of large language models (LLMs) such as GPT-3 and BERT has opened up new avenues in software engineering, particularly in automating and enhancing various aspects of the software development lifecycle. This paper explores the integration of LLMs within software engineering teams, examining their ability to adapt to tasks such as code generation, documentation, project management, and more. The proposed organizational structure leverages LLMs for multiple tasks, resulting in faster code generation, improved networking, and continuous learning. Through case studies and comparative analyses, significant benefits have been identified, including increased productivity and reduced downtime. However, challenges such as biases in training data, lack of creativity, safety risks, and reliance on input quality are also addressed. The paper concludes with recommendations for future research and practical strategies to empower LLM practitioners in software engineering.

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