Exploring the Evolution and Impact of LLM-based Agents in Software Engineering: A Comprehensive Survey of Applications, Challenges, and Future Directions
Introduction
Large Language Models (LLMs) have revolutionized software engineering by enabling tasks such as code generation and vulnerability detection. However, LLMs face limitations in autonomy and self-improvement. LLM-based agents address these limitations by combining LLMs for decision-making and action-taking, paving the way for potential advancements in software engineering practices and towards Artificial General Intelligence.
Research Methodology
A systematic literature review methodology was employed to examine LLMs and LLM-based agents in software engineering, resulting in a robust analysis of their applications and challenges. The final selection included 117 relevant papers, focusing on experimental models and frameworks, and examining performance across various domains.
Key Findings
Results indicated growing interest in LLM-based agents, showcasing their potential to enhance autonomy and self-improvement in software development. The study identified 79 unique LLMs across various software engineering areas and highlighted significant advancements in AI for software engineering, while also identifying areas for further research and development.
Conclusion
The research established a clear distinction between traditional LLMs and LLM-based agents, emphasizing their differing capabilities and performance metrics. LLM-based agents demonstrate potential enhancements to existing processes across various software engineering domains, potentially leading to more autonomous and effective software engineering solutions.
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