Your team is struggling to maintain code coverage. What tools can help?

Junit is one of the frameworks used to write test cases. Running the tests locally, with Junit shows the code coverage. In CI pipelines we can integrate Sonarqube which gives the test coverage, code improvement suggestions and identifies vulnerabilities.

Code coverage ensures that the code is properly tested. If someone breaks the code, then code coverage helps to identify the root cause of the bug. Like which function or statement is causing the issue.

To ensure that you are not shooting in the dark when you are releasing. Code Coverage helps in identifying the workflows that needs manual testing.

Code coverage is important to maintaining a certain level of quality, while ensuring that you are meeting the customer’s needs. Coverage typically provides a measure of much of your code is covered by test cases, providing an important measure of quality. Imagine having 100% of your tests pass, but still the code fails because your tests only cover 10% of your code base. How does that make you feel? It also can be used to track how many requirements are covered by tests, establishing a metric that is useful in reporting milestone progress towards the end goal of the project. In short, code coverage is important both to maintain quality output of the team, as well as to showcase the team’s progress to the customer.

It's give a quality indicator. Know what have you tested and covered give confidence over unexpected behavior. The quality of the codebase as it ensure the long life of the system face to regression and defects

To measure code coverage, you use a tool that watches how much of your code is actually run by your tests. Different tools work with different programming languages, like Jest, Mocha, or Karma. These tools add extra instructions to your code so they can keep track of what gets executed. After your tests run, the tool creates reports and graphs. These show you things like what percentage of your code was tested, which lines were missed, and how coverage changes over time. It's like a report card for your tests, helping you see how well they're checking your code and where you might need to improve.

One thing I’ve found helpful while setting code coverage targets, focus on critical paths and high-risk code areas first. Coverage in these areas is more valuable and impactful for overall code quality. Utilize the tools mentioned to identify these critical areas and prioritize writing tests for them.

Generally speaking, most of the frameworks and IDEs support this and provide the percentage of code coverage for the entire project or some selected portions as well. A good practice a developer can carry is, once the code for any feature is written, the next task should always be to write unit tests for the same. Following a test-driven approach helps to build as an efficient developer!

There are multiple tools available to measure code coverage. For javascript code, Jest and jasmine are the good options. For backend code specifically Java, JUnit and TestNG are the best options available.

Measuring code coverage involves using a tool that can instrument your code and gather data on the execution frequency of each line, branch, function, or statement by your tests. Various tools are available for different programming languages and frameworks, including Jest, Mocha, Istanbul, Karma, Jasmine, etc. These tools can produce reports and graphs displaying the coverage percentage, missed lines, and coverage trends over time, providing valuable insights into your code’s test coverage.

1. Understand the requirements, refer to the documentation provided by the Product owner such as the User Story, and set realistic targets. 2. Keep the room for updates. 3. Assess the risk and make sure to follow security guidelines. 4. Take feedback from seniors and your test reports! And update the coverage accordingly, as needed.

Code coverage is crucial for identifying untested areas, improving software quality, aiding maintenance, assisting code reviews, serving as documentation, and acting as a gatekeeper in CI/CD.

Given that your team is struggling keeping code coverage, there is a problem with the previous code coverage goals. Start from there. The most common issues are: -1) The workload is too high and developers are sacrificing test quality/coverage. This is an issue that must be fixed by better resources allocation. Negotiate with your team, and identify resource leakages. -2)The developers don't find meaning in testing. This often happens when developers are very young or come from a development culture that does not value testing. Giving them Trainings on how and why testing is important and practical examples from your projects is the most effective way to fix this.

Setting code coverage goals is like giving your team a target and keeping an eye on how well you're doing. But there's no one magic number for everyone. It depends on your project—how big it is, how complicated, and how risky. People often say it's good to aim for at least 80% coverage, but it's not just about quantity. You should also care about how good and relevant your tests are. To keep track of your goals, you can use tools like Codecov, Coveralls, or Codacy. They help you see how much of your code is covered by tests. The key is finding a balance that works for your project, making sure your tests are both thorough and practical.

Setting code coverage goals is a strategic way to motivate your team and track progress. The ideal code coverage percentage varies depending on factors like project type, size, complexity, testing methodology, and risk tolerance. While a common goal is at least 80% code coverage, the focus should be on the quality and relevance of tests, not just quantity. Tools like Codecov, Coveralls, or Codacy can be used to set and monitor code coverage goals, ensuring a balance between quantity and quality in testing.

I would like to share how we follow to have good test coverage to the apps we develop irrespective of legacy or modernised app is creating sonar rules and configuration to map out projects and also enable webhooks in our project to run a clean build after every PR to raise to protected branch. The aim of doing so is to keep our app with 90% coverage for existing codes and on new lines it should be again with above 95% so that we don't lose any or mess our code for the sake of release. When every team member starts to adhere to this policy they would be uncovering all the edge cases and also they would know the functionality they develop in a better way.

In the current age of AI, It's most appropriate and time saving to leverage tool like Copilot to write more test suites. This not only increases code coverage but also speeds up development.

To increase code coverage, you need to write more tests that cover different situations and special cases in your code. Writing tests can be a bit of a chore, especially for messy or old code. But there are tools that can help make it easier. Try using tools like Jest Snapshots, CodeceptJS, Testim, or Selenium IDE. They can automatically create test cases by recording what your code should do and checking if it actually does it. Another trick is to use tools like Diffblue or Randoop. They look at what your tests are already covering and suggest new tests for areas that might need more attention. These tools make the job of increasing code coverage quicker and less of a headache.

Analyse the test results and check which area need more coverage. Report provides detailed information, like which branch is missing, which statement is missing or which function is missing. Once done, add the specific test cases for low test coverage code.

1. Monitor the code coverage metrics regularly, and perform the adjustments as needed. 2. Integrate with CI/CD to monitor the continuous code coverage with continuous integration. 3. Focus on highly risky areas of your code, and perform the test case for it. 4. Involve with the QA team, and discuss the potential code failures to understand the behaviors when different preconditions or large inputs.

To maintain code coverage effectively, leverage specialized tools designed to analyze and track code coverage metrics. Integrate code coverage tools into your continuous integration and delivery pipeline to automatically monitor coverage levels with each code change. Set coverage targets and regularly review reports to identify areas of code that lack sufficient coverage. Encourage developers to write unit tests for new code and update existing tests to cover edge cases and critical functionalities. Provide training and support to ensure team members understand the importance of code coverage and how to improve it effectively.

🌿📊 Maintaining high code coverage is an ongoing commitment to code quality! 🏆💪 Establish a regular review process to monitor coverage trends and catch any dips early. 📆📉 Integrate coverage checks into your CI/CD pipeline to ensure new code meets coverage thresholds. 🚀✅ Make coverage a key metric in your team's definition of done for each task or feature. ✔️📋 Foster a culture of shared responsibility where everyone owns the health of the test suite. 👥🤝 Encourage refactoring and updating tests as the codebase evolves to prevent coverage decay. ♻️🔧 By weaving coverage into your team's processes and values, you'll maintain a well-tested codebase! 💯🌟

Keeping up with code coverage means making sure your tests match the changes you make in your code. If you forget or ignore your tests, your code coverage goes down, and your code quality might suffer. To avoid this, you can use tools like ESLint, SonarQube, or Codacy. These tools check your code coverage before you finish or combine your code changes and warn you if it falls below a certain level. Another handy trick is to use tools like Travis CI, Jenkins, or CircleCI, which seamlessly fit code coverage into your continuous integration and delivery process. By doing this, you make sure your tests stay relevant, and your code coverage stays a reliable measure of your code's health.

To maintain the code coverage, we should use some pre-hooks such as husky for angular and sonar for Java. It ensures the code coverage is maintained all the time by restricting the developers from committing the code without proper code coverage.

While code coverage is a valuable metric for assessing the quality of your test suite, it's essential to use it wisely to derive meaningful insights. Avoid focusing solely on achieving 100% coverage, as this can lead to writing superficial tests that don't adequately validate code behavior. Instead, prioritize writing meaningful tests that cover critical pathways and edge cases. Use code coverage as a guide to identify areas of code that require additional testing attention, but supplement it with other quality metrics and techniques such as code reviews, static analysis, and manual testing. Strive for a balance between achieving sufficient coverage and ensuring the effectiveness and relevance of your tests.

Now a days AI has become super powerful to do such tasks. Use Chat GPT / Bard wisely to write effective tests, increase code coverage and save time for other dev work.

Code coverage is a crucial metric for assessing the quality of your tests, but it's not the only one. It's important to remember that high code coverage doesn't guarantee bug-free code. It's possible to have 100% code coverage and still miss critical bugs if the tests don't check for the right conditions. Therefore, while striving to maintain or increase code coverage, teams should also focus on the effectiveness of their tests by incorporating techniques like boundary-value analysis, equivalence partitioning, and state transition testing to ensure a more comprehensive test suite.

Many tools and open source are available to generate the Unit Test in less time, some good Unit Code Generator tools are DiffBlue, GitHub Copilot, Bito. And DiffBlue provides the best coverage compared to other tools