AI Pipeline Failures
The AI-Powered Pipeline Failures page empowers DevOps teams to trace and investigate pipeline failures basically in a very effective way.
- Detailed Monitoring: Failed pipelines can be tracked across repositories and branches with insight into affected workflows. Granular Insights: See key information visualized, including author, branch, timestamps, and failure counts.
- Automated error analysis and root cause detection
- Intelligent fix suggestions based on similar issues
- Automated testing and validation of fixes Learning from Successful Fixes across Repositories
This page will provide you with detailed insights into pipeline issues and upcoming AI-powered features, making debugging easier, reducing downtime, and enhancing pipeline reliability.
Pipeline Failure Summary Table​
Overview of failed pipelines: The following table summarizes the information on failed pipelines, including: Repository and Branch Filters: Focus on certain repositories or branches. Pipeline Details: Name of pipeline, number of failures, and branch it corresponds to.
- Author Information: Identify who is behind recent commits either an individual developer or a team.
- Duration: How long the pipeline ran before failing
- Failed At: Timestamp of failure helps track at what time problems arose.
Example Use: Filter pipelines by branch to research failures specific to recent feature development. Make use of the author detail to assign a fix to the responsible team member.
Pipeline Failure Analysis Modal​
The Pipeline Failure Analysis feature in CICube uses AI to easily identify and handle pipeline failures, improve the efficiency in CI/CD pipeline, and make systems more reliable to use.
Instant Root Cause Analysis: It auto-detects root causes of pipeline failures due to flaky tests, misconfigurations, or resource bottlenecks, eliminating guesswork and providing actionable insights to help resolve issues more quickly.
Smart Suggest Fixes: CICube provides intelligent fix suggestions, specific to the failure. Since these suggestions are created based on successful resolutions in similar pipelines, they are highly relevant and effective.
Failure Metrics and Details​
The Pipeline Failure Analysis modal provides detailed information about each failure, including:
- Details of Pipeline: Concerning this, it would include the organization, repository, branch, and workflow.
- Failure Details: Time of occurrence, duration of failure, sequence of continuous failure, and associated logs.
- Links to Logs and Commits: Access detailed logs and commits related to the failure for further investigation.
Proactive Alerts: It proactively notifies teams about critical failures or when AI-driven fixes are available to enable timely responses to issues.
How It Works​
1. Failure Detection: The system detects pipeline failures and logs them in real-time.
2. Root Cause Analysis: AI does the failure analysis to identify particular problems.
3. Suggestion for Fixing: The platform gives a list of possible fixes.
4. Validation: Fixes can be tested directly in CICube to ensure their effectiveness.
Resolution: The fix is implemented once validated, and the pipeline is put back into a state of normal operations.
- Time Saving: Automates failure analysis and resolution to reduce manual debugging.
- Improve Reliability: Finds and fixes recurring issues for stable pipelines.
- Boost Productivity: Reduces downtime and allows for the teams to spend time on feature delivery.
- Actionable Insights: Provides actionable recommendations on data-driven improvement.
Monitoring Failures
- Use the filters to narrow down repositories or branches with frequent issues
- Pipeline failure counts: Concentrate on high failure counts to prioritize pipelines for debugging.
Prioritizing Fixes
- Check pipelines with the most consecutive failures to resolve recurring issues first
- Utilize the "Author" column to assign fixes to the responsible team member