Скачать или смотреть Solving Google Cloud Dataflow's Code Coverage Challenges with Java 17: A Guide to RedisWriteIO

Discover how to address the `code coverage` issues in Google Cloud Dataflow's `RedisWriteIO` after upgrading to Java 17 and ensure your test cases pass the `SonarQube` threshold.
---
This video is based on the question https://stackoverflow.com/q/76950481/ asked by the user 'viveknaskar' ( https://stackoverflow.com/u/10324087/ ) and on the answer https://stackoverflow.com/a/76975160/ provided by the user 'viveknaskar' ( https://stackoverflow.com/u/10324087/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Google Cloud Dataflow's Code Coverage on the new code is not passing the threshold in SonarQube after Java 17 upgrade

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Overcoming Code Coverage Issues in Google Cloud Dataflow with Java 17

Upgrading an application's dependencies often comes with a set of challenges, especially around code coverage in testing frameworks. Recently, after transitioning a cloud dataflow application from Java 11 to Java 17, we encountered an issue: the code coverage on the newly implemented logic within the RedisWriteIO utility class did not meet the expected thresholds in SonarQube. In this guide, we’ll explore the problem in detail and provide a comprehensive guide on how to address it effectively.

The Problem

After upgrading to Java 17, along with the corresponding libraries and dependencies, our tests reported that certain code paths in the RedisWriteIO class were not being covered. Specifically, one block of code responsible for handling transactions was being skipped by the existing unit tests, leading to coverage failures in SonarQube.

The Critical Code Snippet

The block of code that needed to be covered is as follows:

[[See Video to Reveal this Text or Code Snippet]]

This logic is crucial for ensuring that the RedisWriteIO class can properly handle batch processing when more than 1000 records are processed. However, during testing, we found that even when using a test file with 5000 records, this code was not executed. As a result, we needed to adjust our testing strategy.

The Solution

To achieve adequate code coverage and ensure that all paths in the RedisWriteIO class are tested, we took the following steps:

1. Increase the Test Data Size

One effective method to ensure that the relevant code paths are executed is to increase the size of the test dataset.

Here’s how we modified our test case:

[[See Video to Reveal this Text or Code Snippet]]

2. Tips for Future Testing

Adjust Batch Sizes: Ensure that your test scenarios account for various sizes of input data. This will help in triggering the logic that handles batch processing.

Emphasize Edge Cases: Test boundary conditions to ensure that every piece of logic is executed. For example, tests at just below and above batch size thresholds can be very insightful.

Leverage Mocks: Use mocking frameworks to simulate Redis behavior when testing, which allows for control over transaction handling without needing a live environment.

3. Rerun Tests and Validate Coverage

Once the test adjustment is made, rerun your tests to validate that the previously skipped code block is now being covered. Confirm this change reflects correctly in SonarQube metrics to ensure you meet the desired coverage threshold.

Conclusion

Addressing code coverage issues after a major upgrade can be a daunting task, but by strategically adjusting test sizes and focusing on thorough scenarios, you can ensure all important code paths are covered. In the case of our Java 17 upgrade with Google Cloud Dataflow, simply increasing the dataset size resolved the coverage problem with the RedisWriteIO class, allowing us to maintain our code quality standards without regression.

For application developers facing similar challenges, remember that adequate testing is key to robust applications, especially after significant upgrades. Keep pushing those lines of code, and happy coding!