Aligning the design of a system with its implementation improves product quality and simplifies product evolution. While developers are empowered with AI/ML augmented tools and techniques that increasingly assist them in implementation tasks, the abstraction gap between code design limits automation for design tasks. In this position paper, we argue that the software engineering community can take advantage of the experiences built with AI/ML techniques to advance automation in design analysis. In particular, combining multiple techniques shows promise. We summarize research challenges along the way and exemplify two such efforts that apply machine learning to codebases to extract design constructs and detect deviation from intended designs and use search-based refactoring on graph databases.
Fri 15 Nov
|16:00 - 16:30|
|Can AI Close the Design-Code Abstraction Gap?|
|16:30 - 17:00|
|On the Engineering of AI-Powered Systems|
|17:00 - 17:30|
|Software Quality and Context for Rich Source Code Representations.|