Smoke testing is a quick, shallow test pass that checks the most critical functions of a new build, such as launch, login, navigation and checkout, before deeper testing starts. Run it after every build, merge, deployment or infrastructure change. If a smoke test fails, the team rejects the build and fixes it first.
Understanding the role of smoke testing in software development, however, will help significantly. Rather than spending hours running full regression suites on a broken build, smoke testing provides a quick confidence check that core functionality still works.
This 101 guide gives you a need-to-know overview of the process. We’ll answer the big questions: ‘what is smoke testing?’, ‘when should I do it’, and more, and provide some tooling tips and best practices too.
What Is Smoke Testing?
Smoke testing in software development is a quick, initial round of software testing that verifies the most critical functions of your app work after a new deployment. The idea is that, if these functions fail, there is no point continuing with further testing until they are resolved.
So, why ‘smoke’ testing?
The term actually comes from hardware engineering processes. When engineers turned on a new device, they checked whether any smoke came out of any components before moving on to more detailed tests.
Smoke testing your app is a similar concept. Smoke tests aren’t designed to uncover every defect; instead, they focus on cornerstone functionality such as:
- Can the application launch successfully?
- Can users log in?
- Do main navigation menus work?
- Can core forms be submitted?
- Are APIs responding?
- Can transactions be completed?
- Are critical integrations connected?
Smoke tests are usually broad but shallow. They cover many important areas quickly without going into detailed edge cases.
Let’s say you’re releasing a new version of your e-commerce platform. A round of smoke testing should confirm that users can still browse products, add items to the basket, sign in, and complete checkout. If any of these functions fail, the build may be rejected immediately.
What Are the Advantages of Smoke Testing?
The main advantages of smoke testing are four: less time lost on broken builds, faster delivery, cheaper defect fixes, and one shared release checkpoint for development, QA and operations.
Smoke testing is a fast and effective way to quickly test key features, particularly if you’re working in a CI/CD environment, which demands constant iteration and continuous release cycles.
Smoke Testing Stops You from Wasting Time
Smoke testing saves your team hours of wasted effort performing detailed tests on unstable builds. Checking the foundations first improves the reliability of further testing and ensures that any later effort is well-spent.
Smoke Testing Supports Faster Delivery
In CI/CD pipelines, smoke tests act as a sort of quality gate, and that’s important when you’re releasing several times per week. Smoke tests allow you to up your release frequency while maintaining control and confidence.
Smoke Testing Makes Defect Correction Faster and Cheaper
Fixing critical issues earlier is almost always cheaper than discovering them later in system testing or production. Finding and fixing critical defects at the smoke-test stage will save plenty of engineering hours that you can reinvest elsewhere.
Smoke Testing Encourages Collaboration Across the Business
Smoke testing creates a shared checkpoint between development, QA, and operations teams. It’s an objective measure that allows everyone to agree on whether a release is ready to progress.
Smoke Testing in the Software Development Cycle: Where and Why?
You should run smoke tests whenever your software changes in any meaningful way. This includes:
After a New Build Is Created
Whenever engineers create a new build, smoke testing should confirm that the app installs and launches without issue, and that basic functions work correctly.
After Code Merges
When multiple branches are merged into a shared environment, use smoke testing for a quick, meaningful indication of any integration issues.
Before Detailed Functional Testing
Smoke testing should happen before manual exploratory testing, regression testing, or performance testing so your team doesn’t waste time testing something that’s fundamentally broken.
After Deployments
Once software is deployed to staging, pre-production, or production environments, a quick round of smoke tests allows you to double check there are no major functionality issues.
After Infrastructure Changes
Changes to hosting environments, databases, cloud resources, or network configurations can create unexpected failures. Smoke tests help confirm everything still works properly after these updates.
In CI/CD Pipelines
Many organisations automate smoke testing to run after every commit or deployment for rapid feedback and to prevent major issues from building up further down the line.
Smoke Testing in Software Development: Comparison With Other Testing Types
Smoke testing is not a replacement for other testing methods; it’s an early checkpoint that helps teams decide whether more detailed testing should proceed.
Here’s how it sits with other types of testing , to give you an idea of the role it plays in the wider software development process.
| Testing Type | Goal | Depth | When to Use |
|---|---|---|---|
| Smoke Testing | Verify critical functionality works | Shallow and broad | After new builds or deployments |
| Sanity Testing | Confirm specific bug fixes or minor changes | Narrow and focused | After small updates |
| Regression Testing | Ensure existing functionality still works after changes | Deep and broad | Before release or after major changes |
| Functional Testing | Validate features against requirements | Detailed by feature | During QA cycles |
| Integration Testing | Verify systems/modules work together | Medium to deep | During development and QA |
| Performance Testing | Assess speed, stability, scalability | Specialist depth | Before release or optimisation |
| User Acceptance Testing (UAT) | Confirm business readiness | Scenario-based | Final pre-release stage |
Smoke Testing Best Practices
The six smoke testing best practices are: test the business-critical journeys, keep the run to a few minutes, automate it in CI, maintain the tests, define clear pass and fail criteria, and run in an environment that mirrors production.
Focus on Business-Critical Journeys
Smoke testing should test fundamental, high-priority functions; choose your tests accordingly. You might want to focus on login, authentication, or purchase flows, for example.
Keep It Fast
Smoke testing is a quick checkpoint, not an hours-long full execution of your test suite. If it takes any more than a few minutes, slim it down by prioritizing cornerstone functions.
Automate Where Possible
Automating smoke tests in CI/CD pipelines ensures consistency, speed, and repeatability. And, it saves your engineering team time that they can dedicate to other tasks with more business value.
Maintain Test Cases Regularly
Your test suite should evolve with your app. Set time aside to review your test suite, remove outdated tests, and add new ones for critical functionality.
Audits take up valuable engineer time and, being blunt, no one enjoys doing them. AI can help – see below for details.
Use Clear Pass/Fail Criteria
Define exactly what constitutes a failed smoke test. This prevents confusion and helps teams make release decisions quickly.
Run in Realistic Environments
Where possible, execute smoke tests in environments that mirror production to help catch configuration and deployment issues.
Smoke Testing and AI: The Perfect Combo?
Yes. AI suits smoke testing, because a smoke suite must run fast and cost little to maintain, and AI tools cut the time to write the tests and the time to fix them when the UI changes.
Smoke testing in software development is meant to be quick. It’s a checkpoint that makes sure nothing has really gone wrong before you dive into the details.
In other words, the faster the better. This is not an area where you should be wasting valuable engineering hours, if you can help it. In other words, it’s the perfect candidate for automation.
Traditional automation saves some of that time. AI saves more, because it cuts the time to write a test and the time to repair it when the UI changes.
Intelligent Test Selection
AI tools can analyze code changes and identify which smoke tests are most relevant to run on any given release. This cuts execution overheads by adapting your test suite to the release in question.
Self-Healing Test Automation
Traditional automated tests often fail when UI elements change slightly. If you’ve moved a button a few pixels to the left, then spent valuable time fixing subsequent broken tests, you’ll know the struggle.
AI testing tools find the element again from its description, so a moved button or a renamed class does not break the test and nobody spends an hour on a selector fix.
Predictive Risk Analysis
AI systems can examine historical defects, deployment data, and code complexity to predict where failures are most likely. Your team can then strengthen smoke coverage in high-risk areas, contributing to better overall software quality.
Faster Failure Diagnosis
When smoke tests fail, AI can analyze logs, screenshots, and other data to flag probable root causes. Faster failure diagnosis = faster fixes, and faster releases overall.
Natural Language Test Creation
AI platforms like Momentic let you describe a smoke scenario in plain English, and each step runs from that description. You write no selectors, and any person on the team can read a test and review it.
Continuous Optimization
Run history shows which smoke tests fail most often and which critical flows have no test. Use it to add the missing tests and to remove the ones that no longer protect a release.
Momentic: Smart, AI-Native Smoke Testing Automation
Momentic runs a smoke suite from plain-English test steps in CI, and re-resolves a step when its element moves, so a UI change does not break the suite.
How to run a smoke suite with Momentic
Add the smoke label to each smoke test, then run only the labeled tests on every pull request and every deployment:
# checkout.test.yaml
fileType: momentic/test/v2
id: checkout-smoke
labels: [smoke]
url: https://shop.example.com
steps:
- click: Add to cart
- click: Checkout
- assert: The order summary is visiblenpx momentic run --labels smoke --reporter steps -y --upload-resultsA failed test returns a nonzero exit code, so the CI check fails and the build stops. The uploaded run holds the failed step, its screenshot and its trace.
“With Momentic, we’ve caught bugs that would have eluded even our most diligent internal tests.”
Using Momentic , the team at GPT Zero saw an 89% decrease in defect escape rate .
They also saw an 80% acceleration in release cycles, thanks to time savings realized through natural language test creation and self-healing tests.
Want to join them? Get a demo today