Table of Contents
What Does a Failure Mean?
A Failure occurs when a test completes execution successfully but one or more expected results do not match actual outcomes from the application or system under test. Unlike an Error, which prevents a test from finishing due to execution problems, infrastructure issues, or configuration mistakes, a Failure represents a completed test that ran to completion but detected incorrect or unexpected behavior in the software being tested. Failures are the primary mechanism through which automated tests communicate that something is wrong with application logic, functionality, or performance.
Common reasons for Failures include:
- Broken business logic or functionality regressions where recent code changes inadvertently altered existing behavior or introduced bugs into previously working features
- Incorrect input/output handling where the application processes data incorrectly, returns malformed responses, or fails to validate inputs properly
- Unexpected API responses, missing data fields, schema changes, or contract violations where services no longer meet their documented interfaces or expected behavior
- Changes in dependencies, third-party services, or environment configurations that alter application behavior without corresponding updates to tests or application code
- Unmet performance thresholds or validation conditions where response times exceed acceptable limits, resource consumption is too high, or throughput falls below requirements
- UI rendering issues, incorrect visual states, or missing elements in end-to-end tests that validate user-facing functionality
- Data integrity problems where database records, file outputs, or state management don't match expected values
- Security validation failures where authentication, authorization, or data protection mechanisms don't behave as required
In other words, a Failure doesn't indicate a broken test or testing infrastructure problem—it indicates a real problem in how the system performs under defined expectations and requirements. Failures are the signal that your tests are working correctly by catching actual bugs.
Why a Failure Matters
Failures are key indicators of software quality and the primary output of effective testing strategies. They show precisely where the system deviates from its intended behavior and help teams measure progress toward stability, reliability, and quality goals. Understanding failure patterns, frequency, and context is essential for maintaining high-quality software and making informed release decisions.
Consistently tracking and analyzing Failures allows teams to:
- Detect regressions early in development, testing, or staging environments before bugs reach production and impact customers
- Validate the impact of code changes, refactoring efforts, or configuration updates by comparing pre-change and post-change failure rates
- Prioritize fixes that affect critical user-facing functionality, high-traffic features, or business-critical workflows over less impactful issues
- Measure product quality objectively through failure rate trends, test coverage metrics, and quality gates that prevent low-quality code from advancing
- Increase confidence before release cycles by ensuring failure rates are within acceptable thresholds and all critical tests pass consistently
- Identify problematic areas of the codebase that generate disproportionate failures, indicating need for refactoring or additional testing
- Correlate failures with specific commits, deployments, or configuration changes to accelerate root cause identification
- Benchmark quality improvements over time by tracking how failure rates decrease as teams address technical debt and improve code quality
By analyzing failure patterns over time, across environments, and by test category, teams can distinguish between isolated incidents that represent one-off bugs and systemic issues that affect multiple components, services, or user workflows—enabling more strategic remediation efforts.
Failures in Testkube
In Testkube, a Failure is reported and tracked when a test run finishes execution successfully but returns a non-passing result, indicating the application didn't meet test expectations. These results are stored persistently with comprehensive execution metadata, including detailed logs, stack traces, screenshots, environment details, configuration snapshots, and related Kubernetes events that provide full context for investigation and debugging.
Testkube provides comprehensive visibility into Failures through:
- Execution Details: Each test's pass/fail state, execution duration, individual assertion results, detailed error messages, and complete execution logs organized for easy navigation
- Analytics Dashboard: Aggregated failure counts, failure percentages, pass rates, and historical trends visualized over time to identify quality patterns and regressions
- Filtering and Insights: Powerful tools to group, sort, and filter failures by test type, test suite, environment, time period, execution status, or custom tags for targeted analysis
- Comparison Views: Side-by-side comparison of passing and failing executions to identify what changed between successful and unsuccessful runs
- Integrations: Seamless exporting of failure data to CI/CD systems, observability platforms, incident management tools, or custom webhooks for deeper analysis, automated alerting, and workflow automation
- Artifact Storage: Preservation of screenshots, videos, network traces, performance profiles, and other diagnostic artifacts generated during failing test executions
- Root Cause Attribution: Linking failures to specific code commits, deployments, configuration changes, or infrastructure events that may have triggered the failure
This comprehensive approach enables teams to move beyond investigating one-off failures and gain data-driven insights into overall testing health, quality trends, and areas requiring attention.
How Testkube Highlights and Analyzes Failures
Testkube uses built-in observability, analytics, and intelligent reporting to surface Failures quickly and contextually, helping teams understand not just what failed, but why and how to fix it:
- Visual Indicators: Clear, color-coded pass/fail status in dashboards, reports, and CLI output that makes failure identification immediate and obvious
- Historical Tracking: Compare current failure rates to previous releases, sprints, or time periods to identify quality trends, improvements, or degradations over time
- Failure Rate Metrics: Identify specific tests, test suites, or environments with high failure frequency that require attention, stabilization, or investigation
- Workflow Context: See exactly where Failures occur within multi-step test workflows, understanding which specific steps or dependencies caused the failure
- Failure Grouping: Automatically group similar failures together to identify widespread issues affecting multiple tests rather than isolated problems
- CI/CD Integration: Push failure data, test results, and quality metrics to external systems like Jenkins, GitHub Actions, GitLab CI, or custom pipelines for automation, gating, or alerting
- Trend Analysis: Visualize how failure rates change over time, correlating with deployments, infrastructure changes, or development cycles
- Notification and Alerting: Configure automated notifications to Slack, email, or incident management platforms when failure rates exceed thresholds or critical tests fail
This structured visibility helps teams pinpoint the most critical failing areas, understand failure context quickly, and focus remediation efforts where they matter most for product quality and user experience.