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Agentic AI for Software: thoughts from Software Engineering community

Created by
  • Haebom

Author

Abhik Roychoudhury

Outline

Beyond code generation using large-scale language models (LLMs), this paper explores the potential of AI agents to perform various software engineering tasks, including code generation, testing, program modification, architecture exploration, and requirements understanding and enforcement. We present a vision of an "AI software engineer" in which AI agents, supported by program analysis tools, autonomously make micro-decisions and function as members of a development team. Specifically, we highlight the importance of "spec inference," which captures developers' intent, as a key element in developing trustworthy AI-based software workflows, and the importance of trust in AI agents and AI-based verification and validation (V&V) in automated software engineering.

Takeaways, Limitations

Takeaways:
Presenting the potential of software engineering automation using AI agents.
Emphasize the importance of developer intent inference (spec inference)
The need for AI-based V&V is raised.
Potential for increasing the efficiency of software engineering through AI agents
Limitations:
Immaturity of technology to accurately infer developer intent
The Need for Advancement of AI-Based V&V Technology
The problem of ensuring the reliability of AI agents
Potential for software defects to occur due to errors in AI agents
Further research is needed on applicability in real-world development environments.
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