How BetterBugs.io Helps Debug CI/CD Failures Faster

DevOps has become a powerhouse for managing software operations at scale. Unsurprisingly, the adoption of CI/CD pipelines has skyrocketed. Infact, over the last decade, software companies have been pushing forward to include CI/CD pipelines in their operational workflows as early as they could. According to a research, back in 2012, it took about 586 days on average for companies to decide on adopting CI/CD for their projects, but by 2023 that figure had dropped dramatically to just 91 days.

However, these quicker rollouts come with their own set of challenges, ranging from outages/downtimes to unexpected delays to bugs. Interestingly, the 2020 Atlassian DevOps Trends Survey found that bugs and defects remain the top issue during releases, accounting for 48% of the issues, followed by unexpected delays and outages. 

In this article, we’ve covered the common causes leading to unexpected CI/CD failures, debugging challenges associated with them, and how BetterBugs.io helps debug CI/CD issues much faster.

Here are some common causes leading to CI/CD failures:

Environment Configuration Mismatch

CI/CD servers are typically configured to replicate production environments; however, discrepancies in settings on different systems handling such operations may lead to the “It works on my machine” phenomenon. Differences can include:

• Operating System Variations — Variations in OS or system libraries between developer machines and CI runners.
• Toolchain Version Difference — A different version of compilers, build tools, or interpreters (like specific Python or Node.js versions) can introduce subtle bugs that are difficult to trace without proper logs.
• Container vs. Bare-metal Configurations — Containerized builds may experience limitations related to resource or networking constraints that differ from local VM setups.

Pipeline and Deployment Script Errors

CI systems like Jenkins, GitHub Actions, GitLab CI/CD, CircleCI, and others rely heavily on configuration files (often YAML or JSON). Even the slightest of errors in these files can lead to integration failures. Plus, automated deployment scripts (often shell scripts, Ansible playbooks, or Kubernetes YAML manifests) can also introduce errors. Common reasons include:

• Syntax or Logic Errors — Small mistakes in indentation, unclosed brackets, or typos can cause the pipeline to fail entirely.
• Improper Trigger Configurations — Misconfigured triggers for CI (e.g., for pull requests, commits, tag pushes) lead to pipelines that run at unintended times or not at all.
• Variable Management — Secrets, API keys, and environment variables improperly managed (or exposed inadvertently) lead to failures or security risks.
• Orchestration Failures — When using container orchestration platforms like Kubernetes, misconfigurations (such as incorrect pod specifications, readiness probes, or liveness probes) can lead to rollouts that either crash or render apps unresponsive.
• Infrastructure Service Failures — API rate limits, transient network issues, or mistaken credentials might affect the deployment process.

Also Read: GitHub vs Gitlab

Artifact Management and Versioning Issues

Artifacts generated during the CI process (binaries, Docker images, deb/rpm packages) are typically used in the CD pipeline. Issues or mismatches in artifact management and versions can also lead to failures. Common causes for such issues include:

• Artifact Corruption — If artifacts get corrupted during the build or transfer process, deployments will consistently fail.
• Version Mismatches — Improper tagging, or promotion of outdated artifacts due to pipeline misconfiguration, can lead to deploying unintended versions.
• Immutable Artifacts — Failing to treat artifacts as immutable can lead to accidental modifications in transit, causing checksum mismatches during deployment.

Resource Limitations and Timeouts

Modern CI servers often need CPUs with multiple cores and large-sized memory and disk I/O for compiling code and running tests. Any resource constraints on such fronts can also cause failures, which may not be evident from the start but still can lead to future failures. Common causes include:

• Memory/CPU Overload — High-complexity builds or parallel test runs might exceed available resources, causing job terminations.
• Timeouts — CI jobs often have a maximum execution time. Intensive operations that take too long can be abruptly terminated, resulting in incomplete builds.

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

Network and Connectivity Problems

In distributed deployment architectures, network connectivity plays a critical role. Any issues with the network can lead to intermittent or persistent issues, leading to CI/CD failures. Common causes for the failures include:

• Network Latency and Timeouts — Slow or unreliable network communication during artifact fetching or deployment API calls can halt the pipeline.
• Firewall and Proxy Issues — Misconfigured firewall rules or proxy settings might block outbound connections required for deployments.
• Service Discovery Failures — In containerized deployments, if orchestration tools cannot reliably query DNS or service registries, new versions may never be correctly registered.

Let’s quickly glance over the debugging challenges that you might face if you run into such issues:

• Multiple Environments — Since these pipelines typically rely on a complex ecosystem of tools, code has to be built, tested, and deployed across varied environments. A configuration discrepancy or a subtle dependency change can often lead to failures.
• Diverse Technical Data — Debugging may require an intricate mix of logs, screenshots, video captures, and console details, which must be gathered quickly and without inaccuracies.
• Fragmented Information and Bug Reporting Processes — Data silos can arise when different teams use separate tools for quality assurance (QA), bug tracking, and project management. This fragmentation can lead to inconsistent bug reports that lack the complete context for troubleshooting.
• Manual Copy-Pasting — Traditional methods of debugging often involve manually copying logs, screenshots, and textual data, which are time-consuming and error-prone.
• Inadequate or Inconsistent Testing — Gaps in test coverage allow bugs to persist, flaky tests obscure root causes, and environment-specific failures complicate diagnosis.

Also Read: Bug Life Cycle: Explained

BetterBugs.io is a free and lightweight Chrome extension that enables you to instantly capture, whip up, and share detailed bug reports for issues. These reports come in super handy for debugging CI/CD issues, allowing you to trace the root cause of such issues much faster. 

Using it, you can:

• Take issue screenshots, 
• Record screens for capturing issues in action, or 
• Get a Rewind video session (the last 2 minutes of all browser activities to get a video of issues that just happened). 

Plus, you can add titles, descriptions, steps to reproduce the issue, attachments, priority levels, and personalized video messages (using webcam and mic) to the issues for maximum context, faster turnaround times, and streamlined bug management.  

Besides this, you get the complete technical details auto-attached with the reports, including:

• System environment information
• Console logs (warnings, logs, errors, network errors)
• Navigation steps
• Network requests
• Local storage information  

Then, using a report link, you can share the complete report instantly with others or the connected project management tools (Jira, Linear, ClickUp, Asana, and others). 

The best part? You can integrate it with your Sentry infrastructure (configured with your CI/CD pipelines), which gets you the event logs from the backend right into your bug report. 

Once you connect a BetterBugs project with a Sentry project, you don’t have to juggle tools to check the logs separately. It's all there in the bug report.

And that’s not all! 

BetterBugs.io also packs in an AI debugger with the PRO version (starting at $4 per month/user) that provides:

• AI issue reproduction steps
• AI descriptions for the issue
• Smart bug diagnosis and impact analysis in seconds
• Possible root cause analysis and steps to debug the issue

Here’s a quick breakdown of how BetterBugs.io makes a difference while debugging CI/CD issues:

1 - Rapid Issue Documentation

When a failure occurs in your CI/CD pipeline, every second counts. For rapid issue documentation and quick sharing, just:

1. Download the Extension — Once installed from the Chrome Web Store, creating a bug report is just a click away.
2. Capture the Issue Instantaneously — You can capture a screenshot or video or just get the 2-minute Rewind video session, cutting down on delays and preserving the exact state of the error.
3. Enter Vital Bug Details — With fields for bug descriptions, reproduction steps (manual or AI-generated), priority levels, and custom metadata—everything can be added for easy reproducibility.
4. Upload and Share Instantly — After the upload finishes (within a split second), using the uniquely generated report link, the detailed diagnostic is ready to be shared with zero effort.

Behind the scenes, critical technical information like console outputs, environment information, backend logs, network requests, warnings, local storage state, and others are captured and attached automatically to your bug reports. 

The process not only minimizes time wasted in collecting data but also improves the quality of reports so that nothing gets lost or overlooked during documentation.

2 - Share with Multiple Tools Right from Your Browser

You may often battle with fragmented data residing in different platforms while managing CI/CD issues, making it difficult to form a holistic view of an issue. BetterBugs.io bridges this gap. Here’s how:

• Reports can seamlessly be shared your preferred project and issue-tracking tools with just a few clicks, right from your browser. No more juggling different dashboards or manually copying and pasting data across systems.
• With standardized fields coming from the connected project tools themselves, every bug report maintains consistency as per those defined project fields.
• Once a report is uploaded, all relevant teammates can access it in real time, will all the details in one place.

3 - Smart, AI-Powered Debugging

One of the standout features is the bundled AI debugger within BetterBugs.io. It aims to reduce the cognitive load on developers and QA engineers and enable them to work on what matters most rather than spending hours on data gathering for debugging and analysis. Here’s how:

• The AI debugger analyzes the captured data, generating precise reproduction steps in seconds. This feature is particularly beneficial in situations where the failure seems transient or difficult to replicate.
• The AI capabilities sift through technical logs and environmental details to provide an intelligent analysis of the likely causes, giving the debugging team a head start in troubleshooting.

4 - Ease of Adoption 

BetterBugs.io’s core philosophy centers around ease of adoption. Here’s how:

• With an intuitive and no-code interface, both technical and non-technical team members alike can create cohesive bug reports with minimal guidance.
• With just a few steps—download, capture, document, and share—the tool instantly becomes part of your CI/CD debugging process.
• The detailed reports generated can be adapted and sent to a variety of systems with the full context without additional manual intervention.