Continuous Code Quality: Integrating Static Code Analysis in CI/CD Pipelines

Software development is often a team effort involving several software engineers. While this collaboration allows teams to speed up software delivery, ensuring that each developer contributes high-quality code can be challenging.

This is particularly true in large-scale projects that have multiple developers working on different parts of the application/system.

The more a codebase grows, the harder it becomes to maintain, debug, and understand the code, potentially leading to quality issues. This is where continuous code quality, an essential principle for measuring and improving code quality, comes in.    

Continuous code quality promotes ongoing code analysis and the integration of automated tooling in the code review process. This principle is essential to the DevSecOps philosophy, enabling software teams to identify and fix issues as they arise, rather than waiting until the end of the development cycle.

Explore the importance of continuous testing and the steps involved in integrating static code analysis into CI/CD pipelines.

What is Continuous Quality?

Continuous Quality, or CQ, is a software engineering practice in which isolated code changes are immediately analyzed to ensure they’re high quality and will be easy to maintain in the future before they are added to a codebase. 

Every poor-quality code included in the codebase increases the technical debt. By following the continuous quality principle, software teams can quickly identify and correct issues if they arise. This is a prerequisite for creating and maintaining a healthy codebase. 

CQ boosts productivity during manual code reviews because automated tooling takes care of most of the code analysis, freeing the developer to focus on high-order qualitative reviews instead of worrying about trivial defects.

The tools can also be integrated early in development to fix common issues. CQ enables developers to deliver software that is understandable, reliable, correct, and secure.

What are the Components of Continuous Quality?

CQ comprises several key components in the development workflow that are all crucial for maintaining a clean, secure, and healthy codebase. They are as follows:

• Static  analysis to identify potential issues like security vulnerabilities, performance problems, coding style violations, and bugs so they can be proactively addressed.
  
  
• Manual code review for a more in-depth review of software by the team developers to uncover issues that automated tools may have missed.
  
  
• Different types of testing like units, integration, regression — to ensure correctness of code.
  
  
• Keep an eye on key source code metrics to ensure that the code remains maintainable over time.

Why Continuous Quality is Important

Against the backdrop of increasing software complexity, software teams must implement strategies that ensure high code quality throughout the codebase without slowing down the development process. 

Here are some reasons why CQ is necessary:

• Increasing software complexity and the rise of distributed systems: A growing number of companies, big and small, use the distributed microservice architecture. Rather than having a large team, they opt for multiple smaller teams to handle smaller codebases (i.e., microservices). These microservices rely on one another, yet they are constantly changing and being updated independently several times a day. There is little knowledge of how code changes impact the quality of the system and product. Plus, there is increased difficulty in debugging.
  
  
• There is growing use of software in critical applications: Software is increasingly being applied in essential industries, such as healthcare, security, and financial services. This underscores the need for high-quality software.
  
  
• Continuous deployment: Because delivering high-quality software and doing so quickly is a competitive advantage, many companies now use continuous delivery, a deployment approach that involves frequent software releases, sometimes several times a day. However, this increased frequency can make it challenging to track the changes made when a production incident arises.
  
  
• Scattered product quality data: Teams often use different tools to secure different aspects of the software and infrastructure. As a result, product quality data is dispersed across various stages of the SDLC, making it challenging to assess overall quality. This fragmentation can hinder the ability to obtain a comprehensive view of quality and make it harder to identify trends and patterns.

How to Implement Continuous Quality

At the core of the Continuous Quality philosophy is automation. A critical step to implementing CQ is integrating automated tooling to ensure code quality at every stage of the SDLC process. Test at every stage and receive feedback. 

• Automated testing: The software should be tested vigorously at various levels, including automated tests, unit tests, integration tests, and end-to-end tests. These tests should run automatically as part of the development process. Incorporate build tooling into SDLC to make testing fun, fast, and highly efficient.
  
  
• Code reviews: While automated checks are essential, encourage regular code reviews by peers or senior developers. Manual code reviews help identify issues in code design or issues that the automated tests may have missed.
  
  
• Master plan for testing: Before starting the software development process, design a detailed plan for testing it and map out the contingencies. 
  
  
• Static code analysis: Implement static code analysis to identify potential issues early in development and run the tool as part of your development process.
  
  
• Continuous Integration/Continuous Deployment (CI/CD): Set up a CI/CD pipeline to automatically move your code through the building, testing, and deployment phases. This speeds up software delivery and ensures that new code doesn’t introduce vulnerabilities.
  
  
• Monitoring and Logging: Continuous monitoring is a key aspect of the CQ philosophy. Ensure that your application security platform provides a centralized dashboard to allow you to monitor key security metrics and manage your organization’s security posture.

Steps in Integrating Static Code Analysis Into CI/CD Pipelines

One of the most effective ways to ensure code quality is to integrate static code analysis into the CI/CD pipelines. Here are the steps to take.

Choose a Static Analysis Tool

The first step to implementing static code analysis is choosing the right tool. When evaluating tools, you should only consider those that integrate seamlessly into your development workflow, support the tools/programming languages that your team uses, and give you the ability to customize rules based on your needs.

With that said, note that there are both open source and commercial static analysis tools available, each with its own strengths:

• Open source tools. Examples are Checkov, Semgrep, and CodeQL. These free tools do a good job of finding bugs and security issues in your code. They support many languages and adapt smoothly into CI/CD pipelines.
  
  
• Commercial tools.  Like Codacy, these tools offer centralized management, historical trend analysis, and advanced custom rules for large development teams.

Medium—to large-scale enterprises will benefit from using a commercial tool, as it offers many features designed to simplify and effectively manage application security. 

Configure Analysis in GitHub Workflow 

Integrating code analysis into your Git provider will enable you to scan repositories and pull requests automatically. Some tools can, at your behest, block pull requests that fail to meet your organization’s standards from merging into your main branch.

For example, Codacy status checks, integrated with Git providers like GitHub, analyze pull requests against your defined quality settings and notify you if they meet standards. This allows you to block merging pull requests that don't meet your pre-defined standards. 

Integrate With Code Quality Platform

Tools like Checkov, Semgrep, and CodeQL can identify issues in your code. However, they lack the key features necessary for continuous quality, like centralized dashboards, reporting, and risk remediation.  

A comprehensive code quality platform like Codacy offers all these features and provides the management capabilities to handle findings across teams and projects. The benefits of integrating a comprehensive code quality solution are as follows:

• Advanced features and integrations: Commercial platforms usually come equipped with a suite of advanced features that go beyond the basic functionalities provided by open-source alternatives. These features can include AI-driven insights, deep vulnerability detection, comprehensive reporting tools, and integrations with a wide range of DevOps tools (like CI/CD pipelines, Jira, Slack, and GitHub).
  
  
• Better support and maintenance: They offer robust, professional customer support, ensuring quick issue resolution and consistent updates. The tools are actively maintained by dedicated teams that roll out regular updates, including security patches and new features, ensuring they stay current with industry standards and emerging vulnerabilities.
  
  
• Centralized management and reporting: These platforms often offer centralized management and reporting, which consolidates results from multiple projects and teams into a single view. This enables teams to easily track and understand the overall code quality and security posture of the entire organization from one place.

Shift Left With Linting and IDE Integration

Most mistakes occur in the IDE as the developer is writing the code. For this reason, many code security platforms also provide IDE extensions that directly integrate code analysis into the IDE environment. Integrating this is crucial for identifying issues early in development.

Codacy, for example, has an IDE extension for Visual Studio Code (VS Code) and IntelliJ IDEA.

Another set of automated tools to install are linters. These tools analyze source code for errors, stylistic issues, and potential problems, especially in dynamically typed languages like JavaScript and Python.

Popular linters include ESLint (JavaScript), Prettier (JavaScript), Rubocop (Ruby), StyleCop (C#), Pylint (Python), and Bandit (Python); all supported by Codacy.

Scan Infrastructure-as-code

Beyond analyzing application code, it’s essential to analyze infrastructure platforms like Terraform, CloudFormation, and Kubernetes manifests to catch misconfigurations early. Ensure that your IaC templates meet security standards and cloud infrastructure regulations through the use of custom policies. 

Embed in Pre-Commit Hooks

Using pre-commit hooks to block bad commits is a great way to maintain high code quality and consistency in a project. Pre-commit hooks are scripts that run automatically before a commit is made, allowing you to catch mistakes, enforce coding standards, and improve the overall development process.

You can configure hooks to ensure that all code follows agreed-upon style guidelines (e.g., PEP8 for Python, ESLint for JavaScript). This helps avoid messy or inconsistent code.

Implement Static Code Analysis With Codacy

Codacy uses industry-leading tools to perform automatic static code analysis in over 40 supported languages and infrastructure-as-code platforms. It supports over a dozen open-source static analysis tools, including Checkov, Semgrep, and PMD. 

Codacy also provides IDE extensions, Git integrations, security and risk management dashboards, coding standards, and gate policies, equipping teams with the tools to implement continuous code quality across projects.

Get started with Codacy today for free and take your first step toward writing clean and secure code.