• Yunwen Ye describes agents that assist software engineers using large libraries of components.
• Bernhard Peischl and Franz Wotawa show how to use AI diagnosis tools on software source code.
• Gary Boetticher demonstrates how well AI can learn effort estimations for software projects.
The easy work (such as inventing A* and the idea of STRIPS operators), is over. AI is getting harder. In addition, AI researchers will have to know a lot about many related disciplines.
Fission is promoted by the tendency of AI to be pulled apart by the many adjacent disciplines that join with AI to field practical, large-scale applications in specialized niches.
In fact, some computer scientists and others might go so far as to say, "Why do we need AI as a separate field? One could carve it up, add the parts to adjacent fields and get along perfectly well without it."
• Use case-based reasoning to find model-based components that are relevant to the current development
• Apply search methods or constraint satisfaction tools to optimize verification
• Work within knowledge acquisition and maintenance environments to enable faster model collection and modification
Tim Menzies is the software engineering research chair at NASA's Independent Verification and Validation Facility. His research interests include data mining, software engineering, knowledge engineering, and verification & validation. He received his PhD in artificial intelligence from the University of New South Wales, Sydney, Australia. He is a member of the IEEE and ACM. Contact him at email@example.com; http://menzies.us.