What code review tools provide the best automated testing capabilities?

Gerrit integrates with Jenkins for continuous integration and automated testing. GitHub's pull request feature supports various CI/CD integrations like Travis CI and CircleCI. Bitbucket offers built-in pipelines with support for automated testing frameworks. GitLab provides robust CI/CD capabilities with automated testing and code quality checks.

Code review tools with strong automated testing capabilities include SonarQube, Code Climate, ESLint, and Codacy. These tools integrate automated testing features such as static code analysis, code quality checks, and automated test execution. They help ensure code correctness, identify potential bugs, and enforce coding standards, ultimately improving software quality and development efficiency.

Automated code review helps a lot to prevent unwanted syntax errors and basic errors of logic within the code. The greatest flow for this automation should be integration of the code review automation to the CI/CD which should be one of the stages that need to be passed in order to deploy changes. One of the most easiest way are implementing inside gitlab itself while using any tools inside the codebase based on the architecture. If it's Javascript then for example we can simply use ESLint for basic check. This method can be implemented for all CI/CD integration tools such as Gitlab Pipeline, Jenkins and etc.

Code review is a crucial aspect of the software development process where peers examine code changes for quality, functionality, and adherence to coding standards. Automated testing within code review tools enhances this process by allowing developers to run tests directly within the review environment.

Automation Code reviews helps to optimize the code if the written code is complex Code reusability check can be reviewed during reviews Ensure the code is as per the coding standards

In a busy software team, manual code reviews caused delays and missed errors. Then, they switched to automated testing. Errors were flagged quickly, saving time for developers to focus on more important tasks. The lesson: automated testing not only saves time but also catches errors early, ensuring smoother development.

Automated testing reduces manual effort, ensures consistency, and detects issues early in the development lifecycle. Integrating automated testing into code review tools streamlines the review process and enhances code quality.

Automation testing can execute test cases much faster than manual testing, leading to quicker feedback on software quality and speeding up the overall development process.

Code review tools that support popular testing frameworks like JUnit, NUnit, PHPUnit, or Jasmine provide developers with a familiar environment to write and execute tests. This integration enables seamless testing during code review.

A test automation framework provides QA professionals with different blends of libraries, guidelines, coding standards and methods to create testing tools or platforms

Tools that seamlessly integrate with CI/CD pipelines enhance automation by triggering tests automatically upon code submission. This ensures that every code change undergoes thorough testing before deployment, maintaining the integrity of the software.

Continuous testing is a best practice typically associated with DevOps or Agile methodology. The idea is that smaller and more regular software development life cycles (SDLCs) are beneficial because they provide feedback throughout the entire software delivery process.

Metrics play a crucial role for both testers and development teams. However, it’s essential to exercise caution when using Key Performance Indicators (KPIs). While they provide valuable insights, misusing them for measuring team performance or code quality due management can lead to unintended consequences. Instead of optimizing the metrics themselves, the focus should always be on improving the actual code quality.

Automated testing tools offer more than just test execution; they also provide valuable metrics for assessing code quality. These metrics include test coverage, code complexity, & defect density, among others. By analyzing these metrics, teams can gain insights into the effectiveness of their testing efforts and the overall health of the codebase. For instance, test coverage metrics indicate how much of the code is exercised by automated tests, helping identify areas that may require additional testing. Code complexity metrics highlight parts of the code that are hard to maintain or prone to errors. By leveraging these metrics, teams can make data-driven decisions to improve code quality & enhance the reliability of their software products.

The only way to "measure" improved/worsening code quality is from changing cycle time. The main goal of improved quality is to maintain ability to quickly and efficiently make changes.

Robust code review tools offer insights into code quality metrics such as code coverage, cyclomatic complexity, and maintainability index. Automated testing features contribute to these metrics by assessing test coverage and identifying areas that require improvement.

Automated testing should be complemented with best practices for maximum effectiveness. This includes writing clear, maintainable code, maintaining good test coverage, and keeping tests up-to-date with code changes. Reviewing test results carefully and addressing issues promptly is crucial. Combining automated testing with human insight during code reviews creates a robust defense against bugs and maintains high software quality.

The best code review tools promote best practices by encouraging developers to write testable code. They provide features like test case templates, code linting, and static analysis to facilitate the creation of high-quality tests.

When evaluating code review tools for automated testing capabilities, consider factors such as ease of integration with existing workflows, support for various programming languages, scalability, and community support. Additionally, assess the tool's reporting capabilities to ensure comprehensive test result analysis.