4 Places to Add AI in Your CI/CD Pipeline to Ship Faster

Every week, a new coding assistant launches, promising to make developers 50% faster at writing code.

All you need is a technical specification and you can build and deploy to production without doing any actual coding. Teams are shipping more code than ever before. The irony of it, though, is that coding faster doesn’t necessarily equate to faster releases.

AI-generated code isn’t always secure. They could also produce flaky tests that could fail randomly, delaying your release confidence and velocity. So, how can you use AI to write code and release it faster?

Let’s discuss four key areas of your CI/CD pipeline and AI tools you can integrate into your workflow to ship quality products faster.

AI Code Assurance

We trust AI code a little too much.

It's not like it’s our fault, to be honest. I mean, if it gets the solution most of the time, you trust it a little bit more every time, and It's only a matter of time before we simply accept AI code suggestions without reviewing them.

AI-generated code isn’t perfect. It’s all predictions, after all. So it is only natural that it generates buggy code sometimes. The problem is the code usually looks so good that they’re difficult to spot. This could lead to serious consequences, especially if they occur in sensitive features like data or payment processing features.

The good thing is we can use AI to scrutinize AI with tools like SonarQube’s AI Code Assurance. AI Code Assurance is a feature by SonarSource designed to help developers and organizations become more confident in their AI-generated code. By enabling teams to integrate this tool into their CI/CD pipelines, they can automate enforcing high quality and security standards. All you have to do is configure your project and integrate SonarQube into your CI/CD pipeline following the platform's walkthrough, and you're all set. This allows for deep analysis of all AI-generated code, enabling you to catch bugs that might have slipped through standard code reviews.

By adding a quality gate step into your CI/CD pipeline, every piece of code, regardless of whether it was written by humans or AI, must pass the check to make it into production.

Security Testing

Due to limited resources, many startups ignore security measures.

Between trying to satisfy customers and investors, security testing often takes a back seat, leaving small startups more prone to attackers. In fact, many times, vulnerabilities are only detected after incident reports from users. By then, either user data is already compromised, or engineers are pulling late-night shifts on a weekend to put out fires.

To shift from a reactive to a preventive approach, integrate AI security tools as part of the security audit phase of your CI/CD pipeline. This way, you can scan for vulnerabilities with every code push or merge to development. One effective tool for this is the DeepCode CLI by Snyk. Integrating Snyk is as simple as adding an API key to your CI configuration file and including a security scan step in your CI workflow.

By consistently scanning your code for potential security vulnerabilities, you can build features faster and more confidently. This enables you to focus on satisfying your users as quickly as possible without worrying about their security.

Self Healing Tests

Maintaining tests is time-consuming. Maintaining tests as a frontend engineer is even more time-consuming.

Imagine adding a new feature and pushing it, but your code fails not because your feature is broken but because you moved a div. No wonder frontend developers dislike tests. Product code requires consistent shipping, and maintaining tests can create a bottleneck for that.

To solve this, implement a self-healing feature for your tests in your build phase. Self-healing is the ability for your tests to adapt to a change in your UI elements (like a button being renamed from “Register” to “Sign up”) during testing to prevent test failures. Having self-healing in your pipeline means that your testing phase detects the change in the UI and modifies the tests to work with the modified element, preventing your build from failing due to deprecated tests.

MagicPod is a testing tool that offers this feature. Simply integrate it into your CI/CD pipeline, and you can start running self-healing tests for your front-end applications. This will save you time and allow you to quickly introduce new features to users.

It is important to note, though, that MagicPod’s self-healing tests have some limitations. For instance, they only work for batch test runs, not single test cases. So, you should check out its limitations to see if they align with your project’s testing workflow.

Monitoring With AI

Which would you choose? Monitor your application post-deployment and configure alerts so you can respond when issues occur or predict anomalies and prevent issues before they happen.

No doubt you’d pick the latter.

AI tools like Harness can help you do that. You can integrate Harness into your existing monitoring infrastructure, like Prometheus or Datadog, to build an understanding of your application's normal behaviour. While your app runs, it compares the current behaviour against historical behaviours.

Whenever an anomaly in your application's behaviour pattern is identified, it is flagged, allowing you to respond before it escalates into issues that affect your customer experience.

Don’t Limit Yourself to Code Assistants

Code assistants are great! They help us build products faster than we ever thought we could.

However, writing code is just one part of building successful products; you also need to release it to your users quickly! So, don’t limit yourself to AI code assistants to build faster. Keep exploring, finding different areas of your quality assurance workflow that you can optimize.

References

• AI in CI/CD Pipelines: How to boost software delivery with the power of algorithms
• AI Code Assurance
• Transforming Security Testing With AI: Benefits and Challenges
• What is the Self-healing feature?