<>
Practical Solutions and Value in AI-driven Software Engineering:
1. Addressing Software Complexity:
AI, especially Large Language Models (LLMs), automates code generation, debugging, and testing.
2. Enhancing Developer Productivity:
Tools like LLM-based models automate tasks like code summarization and bug detection, reducing errors and improving speed.
3. Introducing Innovative Framework:
A new framework by multiple universities and Huawei leverages LLM-driven agents for tasks like code generation and debugging.
4. Improving Workflow Automation:
The framework uses modules for perception, memory, and action to automate complex workflows in software engineering tasks.
5. Overcoming Performance Challenges:
Challenges include addressing hallucinations by LLMs and optimizing multi-agent collaboration for enhanced system performance.
