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Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Use Datadog Continuous Testing to release with confidence
2022-10-19 · via Datadog | The Monitor blog

Testing early and often in the development cycle is a must for ensuring that your application meets user expectations. Poor performance and errors can alienate users and prevent you from achieving crucial benchmarks and OKRs. Additionally, having to constantly implement fixes after new, under-tested features are added can fatigue developers and strain your resources, making your organization less nimble overall. By integrating testing at every stage of the application development process, you can catch critical issues before release and help ensure the best possible user experience on every device and browser.

Many modern test-automation tools create silos between quality and development teams, however, making testing time-consuming and frustrating. For quality analysts (QAs) and quality engineers (QEs), using separate tools for creating tests and analyzing performance metrics makes it hard to identify root causes. And for developers, creating and maintaining tests can feel like a tedious task disconnected from the work they’d rather be doing.

Datadog Continuous Testing provides efficient and reliable testing that works seamlessly with the rest of your CI/CD pipelines. This enables you to spend less time configuring tests and more time building new features that you can ship with confidence. By using Continuous Testing, you can:

Easily view a list of your recent tests alongside batch metrics and status summaries.

Easily create effective tests with complete user-scenario coverage

Datadog Continuous Testing helps you quickly create comprehensive tests. With our codeless web recorder, you can design tests by interacting with your application’s UI as a user would. All you need to do is step through the scenarios you want to analyze. The codeless web recorder will automatically record the actions you take and the pages you visit, streamlining test creation. By reducing the amount of scripting needed to generate tests, the codeless web recorder also reduces the potential for human error and simplifies the process of onboarding new engineers with varying degrees of technical experience.

Additionally, Continuous Testing allows you to save even more time and effort by running parallel tests on critical workflows across multiple environments. You no longer need to provision extra resources or carefully schedule your runs in order to avoid conflicts with other teams running tests in CI. You can reuse the same test suites originally created for production monitoring in other environments, which means you don’t need to duplicate work you’ve already done. Plus, you can leverage cross-browser testing to ensure full user coverage across devices.

Let’s say you want to perform testing on an existing purchasing app to verify that it works with your company’s new inventory management service. You can easily create a purchasing scenario with the codeless web recorder and then use parallel cross-browser testing to ensure the service works for all your users. Let’s also say that, after completing these steps, you discover that the test performs as expected on all browsers except Firefox. At this point, you can easily pivot from the failed Firefox test run to Datadog APM or RUM to view associated metrics, traces, logs, and errors, helping you investigate the issue further.

Use session information—including browser, device, and location data—to analyze your recent runs.

Ensure the reliability and resilience of your tests

Continuous Testing comes with self-healing browser tests that automatically adapt to interface changes in your application. If you change the position of a checkout button or add new menu options, for example, the test will update itself in response. As a result, your tests are less likely to break when new features are introduced. This built-in adaptability and resiliency for tests leaves you free to focus on innovation instead of maintenance, enabling your development teams to increase overall productivity.

You can also use automatic test retries to reduce the false positives that come from flaky tests. Too many false positives often leads to alert fatigue, causing you to ignore or downplay warnings even when they might be legitimate. By re-running failed tests, Continuous Testing helps ensure that you receive the most consistent, accurate results and get notified only when something actually appears to be broken. You can then focus your energy on the alerts and issues that truly matter.

Set automatic test retries to reduce false positives from flaky tests.

Integrate tests seamlessly with a variety of platforms

To help you incorporate new tests into your existing workflows, Continuous Testing integrates with a number of best-in-class tools, including GitHub, Azure DevOps, CircleCI, and Terraform. This broad compatibility with CI tools allows you to add testing to every stage of your development cycle for true end-to-end visibility. Additionally, by making testing available with the tools and workflows your teams already use, you can streamline adoption of shift-left strategies, allowing you to identify and remediate issues before they reach your users.

Continuous Testing also integrates with features throughout the Datadog platform, so you can easily perform in-depth investigations into issues surfaced by your tests. Using APM and RUM alongside Continuous Testing gives you extra context for your troubleshooting via user and performance data, such as related network metrics and key session attributes. Plus, you can use Continuous Testing and CI Visibility together for comprehensive insight into both your pipelines and the tests running within them.

For example, if you receive an alert notifying you that a number of your tests have been failing, you can investigate the cause via the test overview panel. Here, you’re able to access a list of your recent runs. By clicking into the failed runs and following them step by step, you can analyze relevant CI information and see additional details about those failures. You might discover, for instance, that the runs all failed at the payment stage. Accessing the related resources (such as image, CSS, or JS libraries) and traces for this step might help you identify that your application is having trouble applying the coupon codes and discounts you’re trying to test. You could then view traces and logs directly from the test run to start troubleshooting and fixing the problem.

Access related information—such as resources, traces, and CI pipeline details—for each step of your runs.

Continuous Testing for complete visibility

Datadog Continuous Testing helps you integrate quick, reliable testing at every stage of your development cycle. By embracing shift-left testing and thoroughly testing features as you develop them, you can deliver the best possible user experience upon release—and with fewer resources and less overall maintenance.

To add Continuous Testing to your pipelines, you can get started with our documentation. Or, if you’re new to Datadog, you can sign up for a 14-day free trial today.