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Smoke Testing: the Go/No-Go Filter for Your Builds

Auteur n°3 – Benjamin

By Benjamin Massa
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Summary – To prevent faulty builds from slowing your teams and harming time-to-market, smoke testing acts as a go/no-go filter by running in under 10 minutes a minimal set of critical scenarios—covering service startup, APIs, and key workflows. Placed after compilation and before exhaustive tests, it reduces late-stage regressions, accelerates feedback, and boosts DevOps/QA confidence with success-rate and duration KPIs.
Solution: formalize and automate a focused suite in your CI/CD pipeline, define a go/no-go threshold, and establish a regular review ritual to maintain the filter’s relevance.

In a continuous integration context, each new build must be validated quickly to prevent errors from blocking teams. Smoke testing, or build verification testing, serves as an initial filter by running a limited set of critical checks. In a matter of minutes, it confirms whether a deployment is viable before committing resources to more exhaustive tests. This approach shortens feedback loops, reduces costs associated with late regressions, and secures the CI/CD pipeline. QA, Dev, and DevOps teams gain confidence and efficiency, ensuring a shorter time-to-market without compromising quality.

Definition and Objectives of Smoke Testing

Smoke testing quickly checks a build’s stability before any in-depth testing. It detects critical issues that would block continuous integration within minutes.

Smoke testing, sometimes called confidence testing, involves running a minimal set of scenarios to verify that key features are not failing. It is not an exhaustive functional test suite but rather selected validations to ensure a build has not broken the core of the application.

This step takes place at the start of the CI/CD pipeline, right after code compilation and packaging. It serves as a quality gate before running longer test suites, such as regression tests or full integration tests.

What Is Smoke Testing?

Smoke testing focuses on a small number of critical scenarios corresponding to the application’s main workflows. It acts as an initial filter to quickly detect blocking failures, such as a service failing to start or an unavailable API.

Unlike unit tests, which target small units of code, smoke testing covers end-to-end workflows. Its quick execution, often under ten minutes, helps identify configuration, deployment, or integration errors.

In short, it’s an express health check of the build: if any scenario fails, the build is rejected and returned to developers for immediate correction.

Goals and Benefits

The main goal of smoke testing is to reduce the risk of running in-depth tests on a failing build, which wastes time and resources. By catching major errors early, it optimizes the CI/CD flow and accelerates the delivery of stable releases.

An example: an e-commerce platform implemented smoke testing based on minimal purchase and catalog navigation. The company detected an authentication issue blocking all payments in the first iteration. By reacting before the extended tests, it avoided several hours of needless debugging and reduced its lead time by 20%.

More broadly, the visibility provided by smoke testing reports strengthens trust between teams, limits rollbacks, and improves the perceived quality of releases.

Differences Between Sanity Testing and Regression Testing

Sanity testing is often confused with smoke testing. It focuses on validating specific fixes or new features, while smoke testing covers the global basics of the application.

Regression tests, on the other hand, verify that no existing functionality has been altered by recent changes. They are generally longer and more exhaustive.

Therefore, smoke testing occurs before sanity testing and regression testing as an initial, fast validation step. Without this gate, heavier suites may fail unnecessarily on basic issues.

When and by Whom to Execute Smoke Testing

Smoke testing should be triggered on every build, after a critical fix, or before a pre-production deployment. It can be executed manually or automatically, depending on the pipeline stage.

To maximize its efficiency, smoke testing is inserted at various key points: post-commit, after merging fixes, and before entering a thorough testing environment.

Depending on organizational maturity, you can involve developers, QA teams, or delegate execution to the CI/CD platform. The essential thing is to ensure speed and reliability in execution.

Key Execution Points in the CI/CD Cycle

In a typical pipeline, smoke testing is placed right after the build and containerization step. If you’re using Docker or Kubernetes, this is the moment to verify that containers start without errors and that services communicate correctly.

Post-fix, after a critical bug is fixed, a dedicated smoke test on the impacted areas ensures the patch hasn’t introduced new basic regressions.

Before pushing to pre-production, a more comprehensive smoke test, including database connection checks and simple queries, validates the compatibility of the target infrastructure.

Stakeholders Responsible for Smoke Testing

During prototyping, developers can run smoke tests manually to validate their code changes. This practice encourages immediate ownership.

In more mature organizations, QA teams automate and oversee smoke testing via the CI platform. They ensure the quality of scenarios and alert thresholds.

Finally, a fully automated execution, driven by CI/CD, offers the best guarantee of coverage and repeatability, eliminating risks of human oversight.

Example of Integration in an Enterprise Pipeline

A telecommunications company integrated a dedicated job in GitLab CI to run 12 smoke testing scenarios in under 7 minutes. These scenarios include API connection, notification sending, and backend error handling.

This case demonstrates that a lightweight, well-targeted automated smoke test can run in parallel with the build and provide rapid feedback without delaying the pipeline. The company thereby reduced production failures due to configuration issues by 30%.

Maintenance responsibility for the scenarios was shared between Dev and QA, ensuring continuous updates of checks according to evolving business needs.

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Automation vs Manual Execution

Manual testing offers flexibility and responsiveness for ad hoc validations but is limited in repeatability and traceability. Automation, integrated into the CI/CD pipeline, guarantees speed, reliability, and structured reporting.

The choice between manual and automated depends on the criticality and frequency of builds. At every critical commit or before a production deployment, automation should be prioritized to avoid oversights and accelerate feedback.

However, for prototypes or urgent bug fixes, a manual smoke test may suffice to confirm the application is functional before implementing more formal automation.

Advantages and Limitations of Manual Testing

Manual testing allows on-the-fly adjustment of scenarios, visual inspection of the UI, and immediate reaction to unexpected behaviors. It’s useful in exploratory phases.

However, it suffers from a lack of repeatability and doesn’t always leave an exploitable trace for reporting. The risk of omission or incomplete execution is high under heavy loads or staff turnover.

Updating manual scenarios can quickly become time-consuming as the application evolves, especially for complex workflows.

Implementing Automation

Automation begins with extracting critical scenarios into a test framework (Selenium, Cypress, Playwright, Postman for APIs). Each scenario must be independent and concise.

Next, integrate these tests into the CI/CD pipeline: as a dedicated step after the build or as a parallel job. Logs and result reports are centralized to facilitate diagnosis.

Finally, a clear success threshold (for example, 100% scenario pass rate or an acceptable number of failures) determines whether to proceed or halt the pipeline, ensuring consistent gating.

Example in an Online Travel Organization

An online travel agency automated its smoke testing with Playwright to verify search, booking, and payment flows. All 15 scenarios run in under 5 minutes on GitHub Actions.

This case shows that lightweight automation can secure frequent platform changes under high traffic. Feedback responsiveness improved by 40%, reducing production incidents during booking peaks.

The company maintains these scenarios through a joint weekly review by QA and DevOps, ensuring continuous adaptation to new routes and business options.

5-Step Method and Best Practices

Structuring smoke testing into five clear steps ensures coherence and maintainability. By targeting critical workflows, automating, and defining Go/No-Go criteria, you guarantee an effective gate.

Beyond the method, KPIs and review rituals ensure the scope remains controlled and scenarios relevant, limiting drift and needless maintenance.

The 5 Key Steps of Smoke Testing

1. Identify critical workflows: select core workflows (login, transaction, email sending) that directly impact the business.

2. Write simple scenarios: each scenario should focus on a single validation without unnecessary branching to guarantee fast execution.

3. Automate and integrate: choose an appropriate framework, integrate the tests into the pipeline, and centralize logs and reports.

4. Report clearly: generate automated reports detailing failures by scenario and by environment for quick diagnostics.

5. Define Go/No-Go criteria: specify the required success rate, acceptable number of failures, and actions in case of build rejection.

Best Practices and Gating KPIs

Keep your smoke test suite fast (ideally < 10 minutes). A build turnaround that’s too long discourages the step and reduces its effectiveness.

Prioritize tests based on business risk: weigh more heavily scenarios involving payments, security, or access to sensitive data.

Measure KPIs such as pass rate, average execution time, and number of rejected builds. These indicators help adjust scope and update frequency.

Pitfalls to Avoid and How to Anticipate Them

A bloated test scope sacrifices speed and relevance. Limit yourself to truly impactful scenarios and review them periodically.

Unclear exit criteria generate unnecessary debates. Precisely document success thresholds and failure conditions, and encode them in the pipeline.

Outdated suites become obsolete. Plan a review ritual (e.g., monthly) to validate scenario relevance and remove those no longer aligned with business needs.

Turn Your Test Pipeline into a Reliable Filter

Smoke testing, integrated and automated, becomes a true Go/No-Go filter that safeguards every step of your CI/CD. By applying a five-step method, targeting critical workflows, and relying on clear KPIs, you ensure early detection of major anomalies.

Our contextual and modular approach, based on open source and scalability, adapts to your business and technical challenges. Our experts help you define your smoke testing strategy, automate scenarios, and maintain pipeline quality over time.

Ready-to-Use Checklist for Your Pipeline README

  • ✅ Define critical workflows (login, transaction, API).
  • ✅ Write simple, independent scenarios.
  • ✅ Integrate the suite into CI/CD (dedicated job).
  • ✅ Automate execution and report generation.
  • ✅ Set Go/No-Go criteria (success rate, failure threshold).
  • ✅ Track KPIs: pass rate, execution time, rejected builds.
  • ✅ Schedule a periodic review of scenarios.

Discuss your challenges with an Edana expert

By Benjamin

Digital expert

PUBLISHED BY

Benjamin Massa

Benjamin is an experienced strategy consultant with 360° skills and a strong mastery of the digital markets across various industries. He advises our clients on strategic and operational matters and elaborates powerful tailor made solutions allowing organizations and entrepreneur to achieve their goals. Building the digital leaders of tomorrow is his day-to-day job.

FAQ

Frequently Asked Questions about smoke testing

What is the role of smoke testing in a CI/CD pipeline?

Smoke testing acts as an initial Go/No-Go filter in the CI/CD pipeline. It quickly runs a few critical scenarios to validate the stability of a build before more exhaustive testing, thus preventing long test suites from running on a faulty artifact and speeding up feedback.

How do you determine critical scenarios for a smoke test?

You need to identify the core workflows that directly impact the business, such as user login, API access, or payment. Select a limited number of simple, independent, and high-stakes cases to ensure fast and relevant execution.

Manual vs. automated smoke testing: what criteria should guide your choice?

Manual testing offers flexibility and speed for one-off checks but lacks repeatability and traceability. Automation, when integrated into CI/CD, ensures consistency, structured reporting, and time savings on every critical build.

At what point in the pipeline should smoke testing be integrated?

Smoke testing runs immediately after the build and packaging stages, before any in-depth testing. It can also be triggered after a critical fix or before deploying to pre-production to validate the service's stability.

How do you measure the effectiveness of a smoke test suite?

Track KPIs such as scenario success rate, average execution time, and the number of rejected builds. These metrics help you adjust the scope, maintain speed, and prioritize the most critical scenarios.

What common mistakes should you avoid during smoke testing?

Avoid too broad a scope that extends the suite, vague success criteria, and outdated scenarios. Schedule periodic reviews to update tests, document Go/No-Go thresholds, and maintain fast execution.

How do smoke testing and regression testing differ?

Smoke testing focuses on a few critical cases at the start of the pipeline to validate the build's health. Regression tests are more exhaustive and longer, aiming to verify that no existing functionality has been deeply altered.

Which open-source tool do you recommend for automating smoke testing?

Frameworks like Playwright, Cypress, or Postman for APIs provide strong automation capabilities, CI/CD integration, and reporting. The choice depends on your stack, scenario complexity, and pipeline maturity.

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