What Is Smoke Testing in Software Development

Smoke testing is a preliminary check that verifies whether a software build’s core functionality works correctly. Teams run these tests after creating a new build or deploying code to see if the application is stable enough for more thorough testing.

The name comes from hardware testing, where engineers plug in a new device and turn it on. If smoke comes out, they'd turn off the power immediately—no further testing is needed. Software smoke testing follows the same logic: if basic functions fail, stop testing and fix the build.

Smoke tests focus on essential rather than detailed features. For example, a banking app smoke test might check if users can log in, view their account balance, and navigate to different sections without errors. If these core functions work, the build passes and moves to comprehensive testing.

Key characteristics of smoke testing:

• Broad but shallow: Covers many features at a surface level
• Fast execution: Usually completes in 15 minutes or less
• Pass/fail results: Either the build is stable or it isn’t

Automated or manual: Can run with scripts or human testers

Smoke testing prevents teams from wasting time and resources on unstable builds. When a build fails basic functionality checks, it signals that deeper testing would likely uncover many more issues.

Running smoke tests early in the process creates a quality gate. Builds that pass can proceed to regression testing, user acceptance testing, and other comprehensive checks. Builds that fail get sent back to developers for fixes.

This approach saves significant time in continuous integration and continuous deployment (CI/CD) pipelines that leverage techniques. Instead of running a full test suite that might take hours, teams get feedback in minutes about whether a build is worth testing further.

Common triggers for smoke testing:

• New code deployments
• Major feature releases
• Database migrations
• Configuration changes
• Third-party integration updates

Teams use different smoke testing approaches depending on their workflow and automation capabilities. Each type serves a specific purpose in the development process.

Manual smoke testing involves human testers following predefined test cases. Testers manually click through core , enter data, and verify expected results. This approach works well for and exploratory testing, but takes more time.

Automated smoke testing uses scripts to run tests without human intervention. Tools like Selenium, Cypress, or Playwright can automatically navigate web applications, click buttons, fill forms, and check responses. Automation enables faster feedback and consistent test execution.

Hybrid smoke testing combines manual and automated approaches. Teams might automate repetitive checks, like responses and database connections, while manually testing user interface elements that require visual verification.

Effective smoke testing follows a structured approach that balances speed with coverage, similar to methods that aim to deliver early results. The process starts with identifying what to test and ends with clear pass/fail decisions.

Step 1: Identify critical user flows using data. Tools like reveal which paths users take most frequently and where they encounter problems. Focus smoke tests on high-traffic flows that directly impact or business metrics.

Step 2: Create simple test cases that cover end-to-end workflows without diving into edge cases. Write scenarios in a "Given-When-Then" format: Given a user is on the login page, When they enter valid credentials, Then they reach the dashboard.

Step 3: Set up test data and environments that mirror production conditions. To reduce test flakiness, use stable test accounts, clean databases, and consistent configuration settings.

Step 4: Execute tests and collect results with clear pass/fail criteria. Document any failures with screenshots, error messages, and reproduction steps to help developers diagnose issues quickly.

Step 5: Make go/no-go decisions based on results. Passing smoke tests allows the build to proceed to further testing. Failing tests trigger a rollback or activation of a before additional testing begins.

Different tools serve different smoke testing needs, from simple API checks to complex user interface automation.

Web automation tools like control browsers programmatically to test web applications. Selenium supports multiple browsers and programming languages, making it versatile for cross-browser smoke testing.

Modern testing frameworks such as and offer built-in features for smoke testing, including automatic waiting, network stubbing, and detailed error reporting. These tools often integrate easily with CI/CD pipelines.

API testing tools like or verify that backend services respond correctly to requests. API smoke tests can check database connections, authentication systems, and third-party integrations without involving the user interface.

Unit testing frameworks, including (Java) and (Python), can run smoke-level tests on individual components or services. These lightweight tests verify that core business logic functions correctly after code changes.

Continuous integration and deployment workflows rely on smoke testing as a quality gate. These automated checks run when developers push code or deploy to new environments.

In typical CI/CD pipelines, smoke tests run immediately after the build and deployment steps, often protected by to limit risk. A passing smoke test signals that the application is stable enough for additional automated testing or manual QA review.

Pre-deployment smoke tests run in staging environments to catch issues before production releases. These tests verify that new code integrates correctly with existing systems and dependencies.

Post-deployment smoke tests run against live production environments to confirm successful deployments. These synthetic checks monitor key user flows and alert teams to immediate problems.

Pipeline configurations often include parallel smoke test execution to reduce overall runtime. Different test suites can run simultaneously across multiple environments or browser configurations.

These three testing types often get confused because they can overlap in timing and scope. Understanding their differences helps teams choose the right approach for each situation.

Smoke testing happens first and covers core functionality broadly. It answers the question, "Is this build stable enough to test further?" Smoke tests run on new builds regardless of what has changed.

Sanity testing happens after smoke testing passes and focuses narrowly on recent changes. It answers the question, "Does this specific fix or feature work as expected?" Sanity tests target particular areas that were modified.

Regression testing happens later in the process and covers existing functionality deeply. It answers: "Did our changes break anything that was working before?" Regression tests rerun comprehensive test suites to catch unintended side effects.

Smoke testing provides valuable early feedback but has clear boundaries in what it can accomplish.

Primary benefits include:

• Early problem detection: Identifies major issues before extensive testing begins
• Resource efficiency: Prevents wasted effort on unstable builds
• Fast feedback loops: Provides quick pass/fail signals for development teams
• Automated quality gates: Enables continuous delivery with basic quality checks

Key limitations include:

• Surface-level coverage: Misses deep integration issues and edge cases
• False confidence: Passing smoke tests doesn’t guarantee production readiness
• Limited scope: Focuses only on happy path scenarios, not error handling
• Maintenance overhead: Automated smoke tests require ongoing updates as applications evolve

When they understand these tradeoffs, teams get the most value from smoke testing and complement smoke tests with comprehensive testing strategies.

Effective smoke testing requires careful planning and consistent execution. These practices help teams maintain fast, stable smoke test suites.

Keep execution time under 15 minutes to provide rapid feedback. Longer smoke tests defeat the purpose of quick quality checks and slow down development cycles.

Focus on end-to-end user workflows rather than isolated features. Test complete scenarios like "sign up → verify email → first login → core action" instead of individual API endpoints or UI components.

Use stable test data and environments to reduce flaky test results. Flaky tests that sometimes pass and sometimes fail erode confidence in the smoke testing process.

Monitor smoke test metrics like pass rates, execution time, and failure patterns. Tools like Amplitude Analytics can reveal which user paths experience the most production issues, helping teams prioritize smoke test coverage.

Unlike point solutions from competitors like Mixpanel or Heap that show limited data views, Amplitude’s unified analytics platform provides comprehensive insights into across all touchpoints, making it easier to identify the most critical paths for smoke testing.

Smoke testing works best when it reflects real user behavior and business priorities. Product analytics help teams identify which features and workflows matter most to users and revenue.

Amplitude Analytics surfaces the user journeys that drive engagement and conversion through , path discovery, and . This data guides smoke test coverage decisions—teams can focus their limited testing time on the paths that impact users most.

After deployment, automated smoke tests work alongside Amplitude’s real-time monitoring to catch issues quickly. and anomaly detection can reveal problems that synthetic tests miss, like gradual performance degradation or regional outages.