The four-year project will run from 2021 to 2024 with the goal to enhance the efficiency and effectiveness of code review by leveraging context and learning from review experience.
The vision and scientific aim at the basis of this proposal are two-fold: First, derive a deep understanding of context concerning code review tasks and leverage it to create new models, algorithms, and techniques to enhance automated cognitive support. Secondly, model code changes as integral components to understanding and automating the code review process as a whole, to learn and transfer review experience.
The project will break new ground on reifying and leveraging context for software development. It will advance machine learning models of code by learning to relate code changes to external context, code review statements, and projects as a whole. And it will support practitioners reducing software defects, speeding up the development cycle, and improving overall maintainability and reliability of software systems on which our society heavily relies.