Stop Testing Everything: Using Analytics to Identify High-Risk Areas

If you’ve ever felt like you’re drowning in test cases with never enough time to test everything and bugs still slip through, you’re not alone!

Different parts of your application have varying levels of criticality and potential impact if they fail. Some components see frequent changes, while others support core business functions. So, rather than test everything, a better approach is to focus your testing where it matters most.

That’s where analytics-driven, risk-based testing comes in. 

Let's explore how data can help you focus on the areas that need your attention the most. But first, let's discuss briefly why it matters. 

Why Risk-Based Testing Matters

Let’s start with a simple scenario. An e-commerce web application has fifteen features, and the testing team is distributed evenly across them. While this approach may seem logical, it is not the most efficient.

As you explore further, you find that two of the fifteen features are critical to customers. One of them, the checkout page, has a history of failing under load. The other is user login, the most used feature in the entire application.

So, why treat them the same as the Vouchers page, which is barely used? Why assign the same number of testers to work on it as the other critical pages? 

Risk-based testing lets you use your resources wisely. Instead of equally spreading your test efforts, you focus on the areas where failure is most likely and most damaging.  This approach helps you allocate time and talent more effectively and improve coverage where it counts.

But how can we identify high-risk areas using data and take action on them?

Identifying High-Risk Areas From Data Sources

Let your data reveal what's truly high-risk.

When you collect and analyse usage data, patterns emerge that highlight high-risk areas, helping you guide your testing focus. Different kinds of data tell you different things about where you should be testing.

Let's consider some of them. 

1. Historical defect reports

Historical defect reports help QA teams identify patterns, such as recurring errors in certain modules, regression-prone features, or defects introduced after specific code changes.

A great defect report links issues back to specific components and includes metadata such as environment, root cause, and affected version, providing you with a risk heatmap.

To get the most value from historical defect data, focus on:

• Defect severity: Are these minor UI issues or critical bugs affecting functionality?
• Module mapping: Are there issues around specific modules (e.g., payment)?
• Defect frequency: How often does a feature break?
• Time to resolve: Do some areas consistently take time to fix?

Tools like Jira and TestRail help transform raw defect data into valuable insights. You could use Jira's custom dashboards to filter bugs by component, priority, and status. For example, you can track recurring defects across releases by setting up  JQL queries `component = Checkout AND status = Done`. 

TestRail allows you to tag test cases by feature and link them directly to defect reports in Jira. You can then analyse failed test cases or test run trends to assess which modules consistently underperform.

2. Production incidents

Monitoring tools provide valuable insights into your software's behaviour that traditional testing might overlook.

System logs, crash reports, and downtime alerts reveal specific errors, exceptions, performance bottlenecks, and the exact environments or actions that led to failures.

For instance, if you were testing a reporting feature in a CRM, tools like New Relic could monitor your system's uptime, enabling you to identify spikes in database connection errors that coincide with users exporting large reports. Your QA team can then create targeted stress test scenarios for the reporting feature to prevent future slowdowns and outages.

Similarly, Datadog allows you to track incident reports and patterns, correlating them with recent deployments. This helps you quickly identify which code changes may have introduced new issues, enabling more focused testing efforts.

3. User behaviour analytics

Use user behaviour analytics to test what users actually use in your application. You might have fifty features, but only five get regular traffic. It would make sense to test those five thoroughly.

You can use Google Analytics to find out which pages and flows of your application users interact with the most. Then, prioritise exploratory and functional testing on those screens/pages rather than spending equal time on low-use pages.

Another effective tool is FullStory, which you can use to replay user sessions to understand real-world paths and pain points.

4. Performance logs

Performance testing data is as critical to quality assurance as functional testing, as performance issues under load can be just as damaging as functional bugs.

When analysing test results, include performance log analysis and focus on key performance signals such as response time under peak load, error rates during load tests, and system resource usage (CPU, memory, database). These metrics reveal how your system behaves under pressure.

Performance testing tools, like JMeter, Grafana, and Prometheus, can provide clarity on how your system behaves under pressure.

Apache JMeter lets you simulate real-world user behaviour under varying load conditions (e.g., 50, 100, or 500 concurrent users) and analyse throughput and latency to identify stress points in your APIS.

You can set up dashboards with Grafana connected to Prometheus, enabling you to visualise metrics such as response times, memory usage, and error rates. Monitor performance over time to catch slow degradation trends before they affect users, and use alerts to flag performance regressions after code changes.

Correlating performance logs with functional test results helps the QA team identify what breaks, when it breaks, and under what conditions it breaks.

5. Code complexity analysis

The more complex your code, the more error-prone it becomes. As more logic and more paths are added, the chances of bugs increase.

Code complexity analysis helps you identify these risky areas early, before defects even occur.  Some key indicators of complex, high-risk code include high cyclomatic complexity, low test coverage, and recent, frequent changes. By monitoring these metrics, you can focus your testing efforts where they'll have the greatest impact.

Software analysis tools like SonarQube and CodeScene are useful for spotting risky, hard-to-maintain code before it causes problems.

Tools like SonarQube and CodeScene provide valuable insights into code quality. With SonarQube, you can scan your repositories to detect code with high complexity and duplication. The "Code Smells", "Bug", and "Vulnerability" metrics help surface areas needing refactoring or extra testing efforts.

Similarly, CodeScene helps visualise hotspots across your codebase where bugs are likely to appear. Its Code Health metric provides a view of your codebase's maintainability. You can monitor changes to code health over time to catch technical debt early, prioritise refactoring efforts, and design more effective test strategies for weak areas.

What does the data say?

As you spend more time testing software and applications, you realise that testing everything is inefficient. Your goal shouldn’t be to test everything but to test the right things.

Using analytics during testing helps QA teams focus on what’s most likely to break or fail, catch more bugs with less effort, and improve user experience where it counts.

The next time you’re looking at a load of test cases, ask yourself, “What does the data say?”

Once you’ve answered that question, use analytics to guide your testing, improve defect detection rates, save time, and deliver higher-quality software.

References:

• Implement Risk-Based Testing Strategies for the Best Test Coverage
• Analytics in Testing
• Predictive Analytics in Software Testing
• How to Analyze Data from Session Replays