Why Every CI/CD Pipeline Needs a Visual Bug Tracker

To fix broken CI/CD pipelines, you’d typically rely on traditional error logs, stack traces, server responses, and server outputs. While using textual information and logs included in the bug reports is most definitely a battle-tested method for resolving CI/CD issues, oftentimes, they lack the complete context and clarity required to resolve the issue in a limited timeframe. In such cases, guesswork becomes your primary tool to fix things. And this often leads to stretched debugging sessions, increased maintenance overheads, deployment delays, and increased operational costs. 

In such scenarios, you are better off using a visual bug tracker that captures everything — from visual proofs to environment information to front and backend logs — offering a well-rounded approach to speed things up for reporting and fixing CI/CD pipeline issues.

Common CI/CD pipeline errors that could benefit from visual bug reporting and tracking methods for debugging purposes:

Build Errors and Misconfigurations

Sometimes, your builds can break or completely fail due to simple misconfigurations: missing environment variables, file path issues, or tool version mismatches. More complex issues can involve misconfigured Docker containers, incorrect build scripts, or circular dependencies in code libraries. These errors are typically logged as text, but they can also lead to visual errors if a critical asset is not compiled correctly. In such scenarios, you’re better off with visual proofs and complete logs instead of just logs.

Also Read: How BetterBugs.io Helps Debug CI/CD Failures Faster

Infrastructure and Resource Limitations

You can also get brickwalled with CI/CD issues related to infrastructure, such as disk space limitations, memory leaks, or even CPU throttling in shared environments. These technical errors not only can crash backend processes but can also affect the frontend. Or, a poorly responding server might send incomplete data or delay rendering, causing the UI to break. Such issues require you to monitor both system logs and the visual outcome of your UI to get a complete diagnostic picture.

Also Read: Reducing Rollbacks in CI/CD Pipelines

Security and Permissions Issues

Security configurations, such as content security policies (CSPs) and cross-origin resource sharing (CORS), might block legitimate resources from loading. In such cases, you may experience situations where a script or stylesheet doesn’t load because of these security settings. While error logs will reveal a blocked request, they do not show you how the absence of that resource impacts the visual layout and functionality. This is precisely where visual bug trackers can step in to help.

Also Read: How to Test Third-party SDKs for Performance and Security Risks

Race Conditions and Timing Errors

Sometimes, your CI/CD pipeline may suffer from race conditions when you see the elements attempting to render before the data is available, causing overlaps or blank spaces. Timing issues in JavaScript, especially when combined with complex animations or lazy-loading, can introduce such transient errors. Detecting and diagnosing these issues requires you to capture the UI state at specific moments during the rendering process.

Also Read: How to Fix JavaScript Heap Out of Memory Error

Microservices and Async Data Loading Issues

For modern app architectures, you often rely on microservices to handle various functionalities. When one service lags or returns unexpected data, the frontend may suffer. For example, you might find that a loading spinner persists indefinitely, or a dynamic chart displays incorrect values. Even if the backend indicates success in API calls, the visual rendering may fail due to poor error handling or synchronization delays. In such cases, visual evidence becomes indispensable to understanding how these sync errors get displayed on the frontend.

Also Read: React Suspense: Work Better with Async Data

Containerization and Environment-related Issues 

Containerization, while beneficial, can bring added layers of complexity to app deployment. Docker images that function seamlessly on a local setup might face unexpected issues when transitioned to a Kubernetes cluster. At times, containers may struggle with inadequate memory or CPU resources, leading to UI components failing to render properly. Additionally, network latency and challenges with volume mounting inside containers can further snowball these disruptions.

Visual Regressions and UI Anomalies

Visual regressions are errors that arise when new code changes unexpectedly alter your user interface. As a result, you might spot misaligned elements, font mismatches, unintended shifts in layout, or incorrect scaling of images. While your automated tests may verify functionality, they do not effectively compare pixel-level differences between builds. Such scenarios can clearly benefit from using issue screenshots or video recordings for visual baselines to determine the exact nature of these regressions.

Also Read: What is Regression Testing?

Asset Loading and External Resource Failures

When third-party resources like images, fonts, or external scripts fail to load, your user interface suffers directly. While logs might show a missing file or a 404 error, you need to see the impact of the broken asset on your layout. Whether the image placeholder distorts the design or a missing icon affects navigation, these failures underline the importance of visual feedback in diagnosing the root cause.

Flaky and Intermittent Tests

You can come across tests that pass most of the time but occasionally fail. These flaky tests can result from async operations, timing dependencies, or network fluctuations. For example, an automated UI test might fail to recognize a rendered component because it loads too slowly on a shared testing server. Flaky tests disrupt your pipeline’s reliability and make it harder to pinpoint whether an error is transient or symptomatic of a deeper issue. In such scenarios, a visual bug tracking tool comes in super handy for faster feedback loops and debugging flows. 

Also Read: What is Bug Tracking?

BetterBugs.io is a visual bug reporting and tracking tool that enables you to report CI/CD pipeline bugs and issues swiftly, with complete context and technical details, including visual proofs of issues, developer logs, environment information, and more.  

It's a free-to-use, lightweight, and intuitive Chrome extension for QA professionals, developers, project managers, and support team members for capturing, documenting, and sharing software issues right from the browser. 

Here’s how BetterBugs.io can help debug CI/CD issues with visual issue documenting capabilities and quick sharing features:

Direct Visual Evidence for the Complete Context 

With BetterBugs.io, you can capture an annotatable screenshot of the issue. You can then add issue details and attachments to your report and share it with others using a report link right from your browser. Or, instead, you can record the screen, annotate while recording, and add a personalized video note so that you don’t have to explain the issue again and again to developers. 

And that’s not all! 

You can also get a two-minute auto-recorded video session of all your browser activities using the Rewind button to capture CI/CD issues that just happened.

Auto-attached Technical Details for Developers

Whenever you capture an issue, BetterBugs.io automatically attaches technical details and environment information to your report for better reproducibility and debugging sessions.  Every report packs in the:

• System information
• Console warnings, logs, errors, and network errors
• Network requests
• Navigation steps
• Metadata (Custom)
• Cookies, local storage, and session data

Auto-attached Technical Details for Developers 

Front + Backend Event Logs

With Sentry integration, whenever you capture an issue, you also get the front + backend event logs (from up to 3 hours before and 3 hours after you capture an issue) right in your bug report. With everything bundled right into your report, you can get to the root cause of the CI/CD issue and fix things up way faster.

AI Description and Debugger

BetterBugs.io also packs in an AI description generator and AI debugger. 

By clicking the “Write with AI Assistant” button in the bug details area, you automatically get the complete issue repro steps of the issue. 

AI Description for Issues 

Once you upload the report to your BetterBugs.io project, using the AI debugger, you can instantly get intelligent analysis and diagnosis of the issue at hand, reducing your cognitive load for manual troubleshooting. Within seconds, you can get auto-generated root cause analysis, suggested solutions, and potential test cases right inside the bug report.

BetterBugs.io AI Debugger in Action

Quick Sharing with Project Management Tools and Issue Trackers: w/ Automation

You can integrate BetterBugs.io with project tools and issue trackers within minutes. So, every time you capture an issue, you can instantly share the complete report link to others or your preferred tool workspace from your browser. 

Or, you can also integrate and automate BetterBugs.io at the:

• Project level 
• Workspace level

Check out the steps for project-level automation here. Here’s a demo for it.

Caption: Steps to Configure Project Level Automation: Linear Example

This enables you to pre-configure details for the integrated tool workspace and instantly share the report to that tool. For example, you can pre-fill the project workspace details for sharing captured bugs,  saving you additional mouse and keyboard clicks. 

Here’s a quick demo for it. 

Project Level Automation in Action: Linear Example