Towards Comprehensible Representation of Controllers using Machine Learning From the point of view of a software engineer, having safe and optimal controllers for real life systems like cyber physical systems is a crucial requirement before deployment. Given the mathematical model of these systems along with their specifications, model checkers can be used to synthesize controllers for them. The given work proposes novel approaches for making controller analysis easier by using machine learning to represent the controllers synthesized by model checkers in a succinct manner, while also incorporating the domain knowledge of the system. It also proposes the implementation of a visualization tool which will be integrated into existing model checkers. A lucid controller representation along with a tool to visualize it will help the software engineer debug and monitor the system much more efficiently.

Can AI Close the DesignCode Abstraction Gap? Aligning the design of a system with its implementation improves product quality and simplifies product evolution. While developers are empowered with AIML augmented tools and techniques that increasingly assist them in implementation tasks, the abstraction gap between code and 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 AIML techniques to advance automation in design analysis. We summarize research challenges and describe two efforts that apply machine learning to codebases to extract design constructs and detect deviation from intended designs and to use searchbased refactoring to make design improvements for extracting functionality.