Nice survey and analysis Paper, bridging LLMs and Software Engineering
Analyzes 115 papers on LLM-based agent technology in SE
https://arxiv.org/pdf/2409.09030
Key points in this Paper 🛠️:
• Presents a framework for LLM-based agents in SE with three key modules:
- Perception: Handles different input modalities (text, visual, auditory)
- Memory: Includes semantic, episodic, and procedural memory
- Action: Contains internal actions (reasoning, retrieval, learning) and external actions (dialogue, interaction with digital environment)
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Key Insights from this Paper 💡:
• Most existing work focuses on token-based textual input, neglecting other modalities
• Lack of comprehensive code knowledge bases for external retrieval
• Challenges in multi-role performance and efficiency of multi-agent collaboration
• Potential for integrating advanced SE techniques into agent systems
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Results 📊:
• Identified key challenges in LLM-based agents for Software Engineering:
- Limited exploration of non-textual input modalities
- Need for diverse agent capabilities
- Hallucinations affecting overall performance
- Efficiency issues in multi-agent collaboration
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