Continuous Quality Signals: Connecting Jira, Zephyr and BugSnag for Risk-Based Testing

Engineering teams want to understand the real health of their applications – not just what was planned or what was tested, but what is actually happening in production. The challenge is that these signals live in different systems, each optimized for a specific part of the delivery lifecycle.

Test execution data, issue tracking, and production monitoring each describe a different aspect of system behavior. On their own, they answer narrow questions about validation, delivery, or stability. What they do not provide is a shared view of how these signals relate to one another or where risk is accumulating across the system. 

This workflow with Atlassian and SmartBear MCP shows how teams can correlate test coverage from Zephyr, defect data from Jira, and production errors from BugSnag to surface risk and prioritize testing and fixes based on real user impact.  

Without that shared context, teams are forced to rely on manual investigation to connect production failures to test gaps or unresolved work. This process is slow, difficult to scale, and often reactive. As systems grow and release velocity increases, important patterns are missed, and prioritization becomes increasingly subjective.

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Continuous Quality Signals Across Delivery and Production 

Many teams rely on test management tools like Zephyr (or QMetry) , delivery and issue tracking tools like Jira, and production monitoring like BugSnag as part of their standard workflow. These systems are effective in isolation, but they are rarely analyzed together. Test execution results often remain disconnected from production behavior, while production errors are triaged without visibility into recent test outcomes or coverage gaps tied to delivery scope. 

This separation creates a fragmented view of quality. Coverage can drift as new stories are delivered without corresponding tests. Production failures may persist while teams focus on issues that are easier to identify rather than those that affect users most. What is missing is not additional tooling, but coordinated analysis across the tools teams already use. 

Running this analysis continuously changes how teams approach the workday. By evaluating test coverage and production behavior together on a regular cadence, teams start each morning with a consistent snapshot of system health rather than a collection of disconnected signals. This removes guesswork from prioritization and reduces the need for ad hoc investigation. 

Evaluating Coverage in Terms of Delivered Functionality 

Test coverage is often reported as an aggregate metric, such as total test counts or overall pass rates. While useful at a high level, these metrics do not explain coverage in terms of delivered functionality, user-facing behavior, or risks. 

By using delivery scope defined in Atlassian’s Jira as the starting point, coverage can be evaluated at the story or requirements level. Stories represent concrete units of functionality that users rely on. 

To understand delivery scope, a project manager working in Jira may start by asking Atlassian’s Rovo:

Retrieve all stories in the current sprint with the status “Done” or “In-Progress”. Include linked defect count so we can understand risk

When these stories are analyzed against test execution data, gaps become immediately visible, including: 

• Stories with no associated tests 

• Stories with tests but have not been executed recently 

• Stories with failing tests that are not clearly tied to delivery risk 
   

A QA engineer or developer reviewing test coverage in Zephyr may ask Claude: 

For this sprint, retrieve linked test cases. Return last execution status, execution date, and pass or fail result. Identify stories with no linked tests or no recent executions.

This approach reframes coverage as a delivery concern rather than a testing artifact. It highlights risk in a way that raw execution metrics cannot, making it easier for teams to understand where validation is insufficient. 

Using Production Errors to Establish Priority 

Production monitoring provides the clearest signal of real user impact. Errors represent behavior that escapes validation and directly affects users. However, raw error streams rarely provide useful prioritization on their own. To establish priority, production errors can be evaluated using signals such as:  

• Frequency of occurrence 

• Severity of failure 

• Number of affected users 
   

When reviewing production behavior, a developer may begin with a simple prompt about BugSnag’s error monitoring using Kiro:

Show me the highest priority errors for the last 24 hours. Rank based on severity, frequency and number of users and affected services.

By ranking errors using these signals, teams can distinguish between failures that are technically interesting and those that materially degrade user experience. Some errors may occur frequently but affect noncritical paths. Others may occur less often but impact core workflows.

Correlating Coverage Gaps with Production Impact 

The strongest insights emerge when coverage analysis and production error data are viewed together. Areas where these signals overlap represent the highest risk. 

A Jira feature or story that lacks sufficient test coverage and is associated with high impact production errors is a clear signal for action. These cases indicate functionality that is both insufficiently validated and is actively affecting users. 

This correlation also reveals blind spots, such as: 

• Production errors in areas with no associated tests, indicate missing validation entirely 

• Recurring production issues in well covered areas may suggest gaps in test effectiveness (realistic scenarios or identifying edge cases) 
   

These patterns are difficult to identify and often remain hidden when tools are reviewed independently. Correlation across systems makes risk visible in a way that individual dashboards cannot. 

When looking across the delivery cycle for testing, defects and production behavior, a quality lead might prompt Copilot:

Analyze the Zephyr, Jira, and BugSnag datasets together to surface gaps and risk. Highlight components where high impact production errors align with weak or missing test coverage and increased defect counts. Call out the defects most likely to affect users and suggest specific areas where additional or improved test coverage would reduce risk.

A Consolidated View for Decision Making

The outcome of this demo workflow is a consolidated view designed to support daily planning and prioritization. By the start of the workday, teams have access to a summary that emphasizes clarity and visibility over volume. 

This view allows engineers and testers to focus on expanding or improving tests where they matter most. Leads can prioritize defect resolution based on real world impact rather than intuition. Product stakeholders gain visibility into how delivery decisions relate to system stability. 

Instead of spending time collecting and reconciling data, teams can begin the day with a shared understanding of risk and clear next actions. 

Practical Impact  

Understanding product health requires more than isolated metrics. It requires connecting what was delivered, what was tested, and what users are experiencing in production. 

By correlating delivery scope, test execution, and production error data across SmartBear and Atlassian tooling, teams gain a clearer view of where quality risk exists and how to address it. Run continuously to ensure this context is available when decisions are made, not after issues escalate. 

The result is not more data, but better context that helps teams focus on the areas that matter most. 

Where to Take This Next 

This workflow is the starting point. The next step is integrating these signals into your workflows, so they are visible where planning and decisions already happen. 

Possible directions include: 

• Publishing the analysis directly to Confluence using Atlassian Rovo or additional MCP. 

• Surfacing coverage gaps and high impact production errors directly on Jira issues. 

• Generating sprint level summaries that show how coverage and production stability change over time. 

• Comparing coverage and production impact across development environments to identify risk patterns.