Keep an eye on these latest automated tools for code review that significantly improve efficiency and quality: Static Analysis Tools (SonarQube, ESLint) scan for bugs and enforce coding standards automatically. AI-powered Assistants (GitHub Copilot, Code Climate) use machine learning to suggest improvements and ensure consistency across your code. Code Review Bots (Danger, ReviewBot) automate routine checks, which saves time during the review process. Peer Programming Platforms (CodeStream, Visual Studio Live Share) facilitate real-time, collaborative coding sessions directly in your IDE.

Some of these tools use third-party static analyzers and linter programs; others may do domain-specific checks. There have been internal tools for getting automated tool output in code review within some large companies (e.g. Google; source https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43322.pdf) and probably others since the early 2010s, but these tools are now becoming more widely available.

Some other collaborative tools are Gerrit, Review Board, Phabricator. These collaborative code review tools offer a range of features to streamline the code review process, improve code quality, and foster collaboration among development teams. The choice of tool depends on factors such as team preferences and project requirements.

Essential for Continuous Improvement: Incorporating code quality metrics into your code review process is vital for ensuring continuous improvement in your software development lifecycle. By consistently measuring various aspects of code quality, teams can identify areas for enhancement and establish benchmarks for future projects. Measuring Maintainability and Readability: Metrics like cohesion and coupling play a significant role in assessing the maintainability and readability of code. Identifying Technical Debt: Technical debt is another critical aspect that code quality metrics can help identify. In financial institutions, where security and reliability are paramount, accumulating technical debt can pose significant risks

Code Quality metrics can help to assess and improve the code quality. Some examples of code Quality metrics are code duplication, function length, code comment usefulness, code modularity, static analysis warnings etc. Moreover Code quality metrics can be prioritized using AI algorithms that involves analysing metrics which are most critical for a given codebase.

Code review feedback is essential for improving code quality, fostering collaboration, and promoting learning within a development team. Feedback should be constructive, focusing on specific issues or areas for improvement rather than general criticism.Acknowledge areas where the code meets or exceeds expectations. Positive feedback helps motivate developers and reinforces good practices. Provide clear feedback using specific examples and references to the codebase. Avoid vague or ambiguous comments. Moreover it should be respectful and timely.

SECURITY ASSESSMENT With the increasing focus on cybersecurity, code review practices are evolving to include more thorough security assessments. This involves identifying and addressing security vulnerabilities, implementing secure coding practices, and integrating security testing tools into the review process.