Key Points

1. The paper presents a framework of LLM-based agents in software engineering, which includes three key modules: perception, memory, and action.

2. The perception module can process inputs of different modalities such as textual, visual, and auditory input, and convert them into an embedding format that LLM-based agents can understand and process.

3. The memory module includes semantic, episodic, and procedural memory, which can provide additional useful information to help the LLM make reasoning decisions.

4. The action module includes internal actions like reasoning, retrieval, and learning, as well as external actions like interacting with the environment through dialogue or digital tools.

5. The paper identifies key challenges for LLM-based agents in software engineering, including the lack of exploration of perception modules beyond text-based input, the need to expand the capabilities of LLM-based agents, the lack of a comprehensive code knowledge base, and the problem of hallucinations in LLM-based agents.

6. To address these challenges, the paper proposes opportunities for future research, such as exploring multimodal perception, enhancing agent capabilities, developing a code knowledge base, and mitigating LLM hallucinations.

7. The paper also discusses the potential of incorporating advanced software engineering techniques, like software testing and package management, into LLM-based agent systems to drive progress in both fields.

8. Multi-agent collaboration is another key aspect, with challenges around managing computing resources, minimizing communication overhead, and reducing reasoning overhead for individual agents.

9. Overall, the paper provides a comprehensive survey of the state-of-the-art in combining LLM-based agents with software engineering, and outlines a research agenda to further advance this emerging field.

Summary

The paper presents a framework for understanding how large language models (LLMs) have been combined with agent-based approaches in software engineering (SE) tasks. The authors first note that many studies combining LLMs with SE have employed the concept of agents either explicitly or implicitly. However, there is a lack of in-depth analysis to understand how these agent technologies are being used to optimize various SE tasks.

The paper introduces a three-module framework for LLM-based agents in SE, comprising perception, memory, and action. The perception module handles inputs of different modalities like text, visual, and audio, converting them into a format the LLM can process. The memory module includes semantic, episodic, and procedural memory, providing additional knowledge to aid reasoning. The action module has internal actions like reasoning, retrieval, and learning, as well as external actions like interacting with humans or the digital environment.

Key Challenges and Opportunities for LLM-Based Agents in SE

The paper then dives deeper into the key challenges and opportunities for LLM-based agents in SE. One key challenge is the lack of exploration of perception modalities beyond just token-based textual input - there is room to leverage tree/graph-based representations of code, as well as visual and auditory inputs. Another challenge is mitigating the hallucinations produced by LLMs, which can degrade agent performance, though agent optimization may also help alleviate hallucinations.

Importance of Knowledge Base and Multi-Agent Collaboration in SE

The paper also highlights the need for an authoritative knowledge base containing rich code-related knowledge to serve as an external retrieval base for agents. Additionally, multi-agent collaboration in SE often requires significant computing resources and communication overhead, presenting efficiency challenges that could be addressed. Lastly, the paper notes the potential for techniques from the SE field to further advance agent technologies, such as adapting software testing methods to identify agent defects, or leveraging software package management for agent systems. However, research exploring the integration of SE techniques into agent systems remains limited, representing a promising area for future work.

Comprehensive Analysis of LLM-Based Agents in SE

Overall, the paper provides a comprehensive framework and analysis of the state-of-the-art in combining LLM-based agents with SE tasks, while identifying key challenges and opportunities in this emerging area of research.

Reference: https://arxiv.org/abs/2409.09030