Software engineering is the integration of principles from computer science to develop software applications. With the advancement of technology, software systems are becoming more complex, leading to challenges in ensuring efficiency and accuracy. The use of Large Language Models (LLMs) in artificial intelligence has had a significant impact on this field, automating tasks such as code generation and software testing.
The increasing complexity of software systems presents a major challenge for developers. Traditional methods often struggle to meet the demands of modern applications, requiring assistance with generating reliable code and detecting vulnerabilities. Current tools such as LLM-based models can automate tasks like code summarization and bug detection, but they are often limited in their scope and need a cohesive framework to address broader software development challenges.
Researchers from various institutions have proposed a new framework using LLM-driven agents for software engineering tasks. This framework includes three key modules: perception, memory, and action. The perception module processes inputs like text, images, and audio; the memory module stores this information for decision-making; and the action module uses this information to perform tasks like code generation and debugging.
These modules work together to automate complex workflows by processing inputs into a format that LLMs can understand, storing different types of information for decision-making, and executing tasks with contextual awareness. However, challenges such as hallucinations produced by LLM-based agents and multi-agent collaboration issues must be addressed to improve performance.
The study also highlights opportunities for future research in resolving these challenges and enhancing the capabilities of LLM-based agents in handling complex software projects. While the proposed framework shows potential, there is room for improvement in reducing hallucinations and improving efficiency in multi-agent collaboration.
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