From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges, and Future

The paper titled "From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future" by Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, and Huaming Chen explores the evolving role of Large Language Models (LLMs) in software engineering, specifically focusing on the transition to LLM-based agents. The goal is to examine how these technologies are applied across various stages of software development, from requirement engineering to software maintenance.

Core Idea

• LLMs vs. LLM-based Agents: The paper differentiates between LLMs and LLM-based agents. While LLMs are powerful on their own, LLM-based agents enhance this power by integrating multi-agent collaboration, autonomous learning, and decision-making frameworks. This distinction highlights the potential of LLM-based agents in advancing software engineering practices.

Technical Approach

• Methodology: The authors conducted a comprehensive survey of existing literature, categorizing research into six key areas:

• Comparison: The study compares tasks, benchmarks, and evaluation metrics for LLMs and LLM-based agents, providing a detailed analysis of their applications and effectiveness.

Distinctive Features

1. Comprehensive Coverage: The survey spans six critical areas of software engineering, offering a broad perspective.

2. Differentiation: Emphasizes the difference between LLMs and LLM-based agents, a key focus of the paper.

3. Novel Techniques: Highlights innovative methodologies such as multi-agent collaboration and autonomous learning.

4. Evaluation Metrics: Provides a detailed analysis of benchmarks and evaluation metrics, offering valuable insights.

Experimental Setup and Results

• Data Collection: The authors reviewed 117 papers relevant to the six key topics. These studies were categorized based on their focus on either LLMs or LLM-based agents.

• Results: The findings are summarized in terms of models used, benchmarks employed, and evaluation metrics applied.

Key Findings

• Requirement Engineering: LLMs automate requirement elicitation and classification effectively. LLM-based agents enhance these capabilities through multi-agent collaboration.

• Code Generation: LLMs excel in generating high-quality code snippets. LLM-based agents improve efficiency through role distribution and iterative optimization.

• Autonomous Learning: Multi-agent systems significantly enhance reasoning accuracy and decision-making compared to single-agent systems.

• Software Design: LLM-based agents demonstrate higher autonomy and flexibility in handling complex design tasks through collaborative frameworks.

• Test Generation: LLMs are effective in generating test cases, while LLM-based agents offer improved reliability through collaboration.

• Software Security: LLM-based agents enhance vulnerability detection and repair through dynamic analysis tools and multi-agent systems.

Advantages and Limitations

Advantages

• Enhanced Capabilities: LLM-based agents offer improved task efficiency, accuracy, and flexibility.

• Broad Applicability: The study covers a wide range of software engineering tasks.

• Detailed Analysis: The research provides a comprehensive analysis of benchmarks and evaluation metrics.

Limitations

• Early Stage Research: Application of LLM-based agents in some areas is still developing.

• Dataset Limitations: The lack of standardized datasets for certain tasks limits generalizability.

• Complexity: Implementing multi-agent systems can be complex and resource-intensive.

Conclusion

The paper provides a thorough review of the application of LLMs and LLM-based agents in software engineering. It highlights the enhanced capabilities brought by multi-agent collaboration, autonomous learning, and decision-making frameworks. The study underscores the potential of LLM-based agents to advance software engineering research and practice.

This survey serves as a roadmap for future research, pointing out current advancements and potential future directions in the field of software engineering.

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