The paper "Resolving Code Review Comments with Machine Learning" by Alexander Frömmgen et al. discusses the application of machine learning (ML) to automate and streamline the code review process at Google. The authors describe how they leveraged recent advances in large sequence models to build an ML-based assistant that suggests code edits to address reviewer comments. This assistant is integrated into Google's code review workflow, where developers receive feedback from reviewers and can apply suggested edits directly to their code. Key contributions include: - Curation of a training dataset from tens of millions of code reviews. - Tuning of the ML model to improve prediction quality. - Design and implementation of the comment-resolution assistant. - User interface refinements to enhance usability. - Qualitative and quantitative results demonstrating the positive impact on productivity. The assistant has been deployed to all Google engineers, addressing 7.5% of all code review comments with applied ML-suggested edits. The paper also highlights the importance of user feedback and the evolution of the assistant's design to improve its effectiveness. The authors conclude by discussing related work and future directions, emphasizing the potential for further improvements in the ML-assisted code review process.The paper "Resolving Code Review Comments with Machine Learning" by Alexander Frömmgen et al. discusses the application of machine learning (ML) to automate and streamline the code review process at Google. The authors describe how they leveraged recent advances in large sequence models to build an ML-based assistant that suggests code edits to address reviewer comments. This assistant is integrated into Google's code review workflow, where developers receive feedback from reviewers and can apply suggested edits directly to their code. Key contributions include: - Curation of a training dataset from tens of millions of code reviews. - Tuning of the ML model to improve prediction quality. - Design and implementation of the comment-resolution assistant. - User interface refinements to enhance usability. - Qualitative and quantitative results demonstrating the positive impact on productivity. The assistant has been deployed to all Google engineers, addressing 7.5% of all code review comments with applied ML-suggested edits. The paper also highlights the importance of user feedback and the evolution of the assistant's design to improve its effectiveness. The authors conclude by discussing related work and future directions, emphasizing the potential for further improvements in the ML-assisted code review process.