In modern software development, it is critically important to understand why a Continuous Integration (CI) pipeline is needed and how it can be built to automatically check application code quality and even suggest fixes for discovered issues. For software developers, especially those working in engineering roles, this knowledge is extremely valuable and directly applicable to everyday work.

Imagine working in a team of software developers on a specific feature. The team follows a Git workflow known as trunk-based development, which is a standard approach in many DevOps-driven environments. In this workflow, the main branch is always kept in a releasable state. Any code change pushed to the main branch triggers an automated CI/CD pipeline.

This pipeline is fully streamlined and designed without bottlenecks, meaning there is no manual intervention at any stage. Code is automatically built, tested, and deployed at least to the development environment. With strong test coverage, the deployment may even reach staging or production without requiring manual testing or code checks. However, for such a setup to function reliably, the team must rely heavily on automated tests to ensure that anything merged into the main branch is safe to release to end users.

Types of Tests in a Release Pipeline

A robust release pipeline includes several types of tests, each focused on a different aspect of the code:

• Unit and integration tests, which validate code logic under various inputs and scenarios.

• Security tests, which scan dependencies and libraries for known vulnerabilities, detect hardcoded secrets, and validate security best practices in the application logic.

• Code quality tests, which examine maintainability rather than functionality or security.

Even if an application works correctly and is secure, poor code quality can lead to long-term problems. Common issues include duplicated code instead of reusable functions, usage of outdated libraries or APIs, potential null pointer exceptions in Java applications, or insufficient test coverage. Code quality tests focus on detecting exactly these problems.

Git Workflows and the Importance of Early Testing

While trunk-based development is often considered an ideal workflow, many teams use an alternative approach known as the feature branch workflow. In this setup, all feature development happens in dedicated branches rather than directly in the main branch. Developers create feature branches, work in isolation, and then submit merge requests.

Merge requests act as gatekeepers. All automated tests run before the code is merged into the main branch, ensuring that only releasable code makes it through. This is crucial because if broken code is merged into the main branch, it can block other developers’ work. Even if they fix their own issues, deployment is still impossible until the original problem is resolved.

However, relying solely on merge request testing has its own drawbacks. If a feature branch accumulates many commits over time, issues may only be discovered late in the process. At that point, fixing them can require significant rework, especially if outdated or insecure libraries were used early on.

Continuous Integration and Feedback Loops

This is where Continuous Integration (CI) plays a key role. CI is the practice of committing small code changes frequently and running automated tests on every commit. This creates a fast feedback loop that allows developers to detect and fix issues as soon as they appear.

For example, if a developer introduces a new library and commits the change, a CI pipeline can immediately flag potential problems with that library. The developer can then fix the issue before moving on. This approach is particularly beneficial for junior engineers, as it helps them learn best practices through continuous feedback.

CI pipelines run on feature branches and focus exclusively on testing rather than deployment. Over time, testing is shifted earlier and earlier in the development lifecycle—a concept known as shift-left testing.

Local Code Checks and the Need for Centralized Automation

Some code issues can be detected even before CI pipelines run. Modern IDEs such as IntelliJ IDEA include built-in inspections that highlight code smells, duplications, and potential improvements while developers are writing code. These tools often provide automatic suggestions, such as updating library versions or refactoring duplicated logic.

However, developers may ignore these warnings or be unaware of certain features. Relying solely on individual responsibility is risky, which is why centralized, automated testing in CI pipelines is essential. CI pipelines ensure that code quality standards are enforced consistently across the entire team.

Multiple Layers of Code Quality Control

Code quality checks typically run at multiple stages:

1. Locally in the developer’s IDE

2. In the CI pipeline for feature branches

3. On merge requests

4. After code is merged into the main branch

Running checks at every stage ensures that issues introduced by integrating multiple developers’ changes are detected early.

Building a CI Pipeline for Code Quality Checks

Now that the importance of CI is clear, the next step is building a pipeline to automate code quality checks. This setup uses GitHub Actions as the CI server and Kodana, a tool created by JetBrains, for code analysis.

Kodana leverages the same inspection mechanisms built into JetBrains IDEs. It scans code, detects issues, and suggests fixes, but runs centrally in a CI pipeline rather than on individual machines. It also supports multiple programming languages such as Java, JavaScript, Python, PHP, and more, eliminating the need for language-specific tools.

The setup requires only two steps:

1. Creating a GitHub Actions workflow for the CI pipeline

2. Creating a kodana.yml configuration file that defines how the analysis should run

Thanks to IDE integration, both configuration files can be generated automatically, minimizing setup time.

Integrating Kodana with GitHub Actions

The CI workflow is configured to run whenever a pull request is created for the main branch. The workflow includes two main steps:

• Checking out the repository code

• Running Kodana analysis using a predefined action

To visualize scan results effectively, the pipeline is connected to Kodana Cloud, which provides a user-friendly interface. Scan results are uploaded automatically and displayed with severity levels, affected files, and detailed explanations.

Reviewing and Managing Code Quality Results

Once a pipeline run completes, the scan results are available through multiple interfaces:

• An overview inside GitHub Actions

• A detailed, filterable dashboard in Kodana Cloud

Developers can inspect individual issues, prioritize high-severity findings, and understand exactly where problems originate.

Automatic Code Fixes in the CI Pipeline

Kodana also supports automatic code fixes. By enabling this feature, the CI pipeline can apply suggested fixes and open a pull request containing those changes. Examples include:

• Removing unnecessary code

• Adding null checks

• Introducing modern libraries such as Lombok

• Refactoring duplicated logic

These pull requests can be reviewed and merged by developers, making the process both automated and safe.

Maintaining Long-Term Code Quality

All scan results are stored over time, creating a history that shows whether code quality is improving or declining. This makes it easier for teams to monitor trends and enforce quality standards across large projects with many contributors, including junior engineers.

Conclusion

A well-designed CI pipeline is essential for maintaining code quality in modern software development. By integrating automated code quality checks into the CI workflow, teams gain early feedback, reduce technical debt, and prevent issues from reaching production. Centralized automation ensures consistency, reduces the burden on senior engineers, and helps every developer produce cleaner, more maintainable code.

Ultimately, CI pipelines with integrated code quality tools form the foundation of reliable, scalable, and professional DevOps practices.