Artificial General Intelligence in Software Engineering: Early Applications and Governance
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I4P103Keywords:
Artificial General Intelligence (Agi), Software Engineering, Code Generation, Explainability, Risk ManagementAbstract
Artificial General Intelligence (AGI) represents a paradigm shift in the field of artificial intelligence research, known as advanced artificial intelligence, which is capable of generalised reasoning, autonomous learning, and domain-free applications, extending beyond the realm of narrow AI applications. Within the software engineering universe, AGI has shown some promising early signs of default and is being deployed in various important moments throughout the development lifecycle, such as code generation, testing, architecture design, and DevOps optimisation. This article explores the guiding principles that define AGI, distinct from conventional AI, and identifies the primary strengths of this technology in the evolving landscape of software engineering. We examine the role that AGI-based tools will play in aiding software development in a more autonomous, creative, and intelligent manner, enabling systems to be developed with the ability to adapt to new needs, detect errors, and even transform older code. We also look at assistants and collaborative multi-agent systems that use AGI to help developers and make their work easier, which improves productivity and the ability to make decisions. The paper talks about these technical advances and the urgent issues they raise about government, ethics, and risk management. Open-sourced decision-making, human control, and governance mechanisms are identified as the critical factors for the secure implementation of AGI. The paper ends by talking about the future of AGI in software engineering. It stresses the need for systems that are not only powerful and flexible, but also explainable, accountable, and in line with human values. This synthesis offers conceptual insights to researchers and practitioners that are practically applicable to the challenges and opportunities of integrating AGI into real-world software development environments
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