CI/CD pipelines are the backbone of efficiency in software development. They help teams test, build, and deploy code quickly. But when these pipelines break, everything slows down — deadlines get missed, and developers are left frustrated as they work to fix things and keep projects on track.
So, why do pipelines break in the first place? Let’s break it down.
Reasons for pipeline failures
A pipeline failure occurs when the automated workflow in your CI/CD pipeline — a series of steps that can include building, testing, and deploying code — does not execute as expected and ends with an error message. This failure can prevent code from being properly built, tested, or deployed, causing delays in software delivery and requiring troubleshooting to resolve.
Pipeline failures can happen for a variety of reasons. Some common causes include:
- Syntax errors: A small mistake in the code, like a missing semicolon or incorrect variable name, can cause the pipeline to fail.
- Failed tests: Unit or integration tests might fail due to broken code, incorrect configurations, or mismatched dependencies.
- Misconfigurations: Incorrect pipeline settings or environment configurations can lead to failed builds or deployments.
There are also more complex issues that add to the challenge:
- Infrastructure-as-Code (IaC) issues: Problems in provisioning cloud infrastructure, such as errors in Terraform scripts or CloudFormation templates, can prevent a successful deployment.
- Kubernetes and GitOps challenges: Misconfigurations in Kubernetes clusters or issues with GitOps workflows (e.g., syncing Kubernetes states with Git repositories) can cause pipeline failures that are difficult to diagnose.
- Long, messy stack traces: When an error occurs deep in the system, stack traces can become long and hard to decipher, especially when they span multiple components or services.
These challenges make troubleshooting more difficult and time-consuming, as finding the root cause often involves sifting through complex logs, reviewing configuration files, and testing different solutions.
The real impact of failed pipelines
When a pipeline fails, it doesn’t just delay your deployment — it brings stress and frustration. Developers are forced to pause their work and dive into troubleshooting, which often leads to a chain reaction of disruptions. This makes it harder to meet deadlines and increases the pressure on the entire team. But why is manual troubleshooting so stressful?
Manual troubleshooting
The time it takes to fix a broken pipeline varies. It depends on things like:
- How well the developer knows the project
- How experienced they are with similar issues
- Their overall problem-solving skills
Manually digging through logs to figure out what went wrong is a tough and tedious process. Logs can come from all over the place, including application errors and system messages, and they’re often messy and hard to interpret. And on top of that, fixing the pipeline usually requires a lot of jumping back and forth between tasks, adding more time to the process.
This is where GitLab Duo comes in. GitLab Duo can sift through all that messy data and spot issues much faster, simplifying the process so you don’t need to be an expert to figure out what went wrong. With AI, fixing your pipelines becomes faster, easier, and much less stressful.
GitLab Duo Root Cause Analysis with generative AI
When your CI/CD pipeline breaks, you don’t have to spend hours manually troubleshooting. Enter GitLab Duo’s Root Cause Analysis (RCA). This AI-powered tool quickly identifies the exact cause of the failure and suggests fixes — right within the DevSecOps platform. No matter how long or complicated your stack traces are, RCA analyzes all the data, breaks it down, and gives you clear, actionable insights.
It tells you exactly what caused the error, provides steps to fix it, and even pinpoints the specific files and lines of code that need attention. And, to make it even easier, it suggests code fixes to get everything back on track. This makes troubleshooting a lot faster and more straightforward.
Keep the conversation going with follow-up questions
With GitLab Duo RCA, you don’t just get answers — you can ask follow-up questions to dig deeper. Want to explore alternative solutions? No problem. You can add more context by referencing other files, issues, or epics in your repo. For example, you could open your .gitlab-ci.yml file in the IDE and ask the chat, “Based on this file, and the analyzed CI/CD pipeline, how would you propose to optimize the pipeline?”
Privacy first – everything stays in GitLab
One of the key benefits of GitLab Duo RCA is that it works right out of the box within GitLab. You won’t have to switch tools or go hunting for external help. Plus, your logs and sensitive data stay secure - there’s no need to send them off to external AI solutions. RCA is seamlessly integrated within GitLab, offering valuable insights without ever compromising privacy.
Get started today
Want to see how AI can supercharge your development process, making it smoother and faster? Dive into our GitLab Duo Enterprise product tour below and discover how GitLab Duo’s AI-powered insights can transform every stage of your development journey — from planning and coding to troubleshooting and deployment. Click the image below to start the tour!
