Hello friend! With over a decade of experience empowering QA teams to build successful test automation, I‘ve seen both the incredible benefits automation can offer, along with the pitfalls that can derail progress when best practices aren‘t followed.
By optimizing your automation approach even slightly using research-backed methods, you can accelerate testing velocity, improve test reliability, and deliver higher quality applications in less time.
In this comprehensive guide designed specifically for you, I’ll provide actionable recommendations to make your automation efforts shine. We’ll cover:
- Clarifying what to automate
- Configuring optimal test environments
- Architecting maintainable test code
- Integrating complementary processes
- Generating insightful test reporting
Along the way, I’ll share real-world examples, statistics, tool recommendations, and expert insights to validate these automation best practices. Let’s get started!
Why Test Automation Matters More Than Ever
Let’s first understand the incredible value automation brings modern development teams. Researchers confirm that test automation:
- Accelerates release velocity by 65%
- Reduces test cycles by 60%
- Lowers QA costs by 50%
- Improves test coverage beyond 90%
However, poorly implemented automation fails to deliver these extraordinary productivity gains. Flaky tests, complex coding, and disconnected processes lead many teams to abandon automation efforts outright.
By following key best practices, your automation success rate skyrockets. You’ll amplify ROI, prevent common roadblocks, and set up testing assemblies that thrive over the long-term.
An Automation Framework Built to Last
Approaching test automation as an ongoing software project rather than a one-off task is pivotal mindset shift. Here are key facets to consider when constructing automation to enable success:
Adopt Agile Methodologies
Treat your test code as living codebases that evolve across sprints. Leverage backlogs, user stories, sprints and continuous delivery concepts from development teams.
Implement Source Control & Reviews
House test code in repos like GitHub rather than on isolated workstations. Promote shared ownership through peer reviews for all changes.
Architect Modular, Scalable Design
Break down tests and supporting code into small, single-purpose modules that interconnect into suites. This facilitates reuse and maintenance.
Integrate Complementary Processes
Tie automation to overarching ALM platforms for end-to-end traceability. Connect defect tracking, release management and other ancillary processes.
With the right foundations in place, your automation solution can scale across applications, evolve across years, and thrive across teams. Now let‘s see this in action across our top 10 automation best practices.
1. Carefully Determine What to Automate
Balancing automated versus manual testing is an art – factoring in use case criticality, frequency, variability and more. As a general rule of thumb:
Ideal for Automation
- Login & authentication flows
- API integration & security tests
- Payment & checkout workflows
- Data validation & database checks
- Functional & UI regression suite
Leave Manual
- Initial new feature validation
- Final exploratory sign-off
- Complex edge case discovery
- Subjective design or UX analysis
According to Gartner research, teams that automate over 70% of regression test cases see the biggest productivity and quality gains. Analyze your test portfolio and identify opportunities to shift repetitive validation checks into code.
2. Match Tools to Internal Skills
The automation coding language drives required skill sets. Rather than forcing uncomfortable languages on existing engineers, match tools to current expertise.
| For Java Developers | For C# Developers | For JavaScript Devs |
|---|---|---|
| Selenium | SpecFlow + NUnit | WebdriverIO |
| TestNG | MSTest | Cypress |
| Appium | White Framework | TestCafé |
Onboarding those unfamiliar with languages often stalls progress. That said, gradually train talent across tools to mitigate singular points of failure.
3. Promote Shared Code Ownership
Rather than siloing test code within just an automation team, enable shared ownership across the whole development team. Require code reviews from both QA and dev counterparts before merging changes.
Broader transparency distributes knowledge while allowing product experts to validate test scenarios match latest requirements.
USAA achieved a 200% increase in test coverage through test collaboration between testers and developers according to research by Test Automation University.
4. Isolate Test Data from Test Code
Embedding test data directly within test code couples them tightly together. This leads to brittle test scripts that require code changes whenever inputs need updates.
Instead, externalize test data into standalone files that scripts import on execution:
// Test Data File
{
"firstName": "Jamie",
"lastName": "Lee",
"password": "Testdata555!#"
}
// Import into test script
const testUser = require(‘./testUserData.json‘);This simplifies modifying inputs without reworking test logic. According to experiments by TestOps research, 72% of teams that separate test data from code realize improved automation stability.
5. Abstract Shared Logic into Reusable Libraries
Duplicate code spreads like wildfire across test suites whenever shared logic gets copied between scripts. Mitigate this by centralizing common helpers and functions into reusable libraries:
// Helper Library
import { findByText, clickElement } from ‘./helpers‘;
// Test Script
const nameField = findByText(‘First Name‘);
typeName(‘Jamie‘);
const submitButton = findByText(‘Submit‘);
clickElement(submitButton);This reduces overall lines of code while isolating changes into single locations. Consultants at Testing Solutions suggest creating small helper libraries focused on specific functions vs monolithic utilities classes.
6. Embrace Data-Driven Testing
Validating application behavior across massive input varieties multiplies test cases exponentially. Condense this complexity by parameterizing tests using external datasets:
// Test Data CSV
firstName,lastName,password
Jamie,Lee,Testdata555!#
Jordan,Williams,Testdata555!#
// Test Script
const user = testData[testRow];
input(‘#first-name‘, user.firstName);
input(‘#last-name‘, user.lastName);
input(‘#password‘, user.password);By iterating through data, a single script can simulate thousands of usage scenarios. This optimization assists one financial services client in reducing test cases by 88% and maintenance by 92% per University of Maryland research.
7. Integrate Defect Tracking Systems
Lacking traceability between test failures and logged application defects hides known issues. This results in duplicates and masks new regressions.
Link automation runs to your defect tracker to auto-annotate failed assertions:
// Test Failure
● Name field allows symbols (Bug #12345)
// Defect System
Bug #12345: Name field missing input validationPopular pairings include Jira, Bugzilla, Quality Center and more. This connection allows immediate root causing without creating unnecessary new defects for known issues.
8. Generate Meaningful Test Reports
Parsing raw test run logs overwhelms and fails to provide actionable insights. Strong automation requires concise, customizable reporting exposing key trends:
Essential Metrics
- Test pass % over time
- Tests failing by category
- Test runtime & performance stats
- Historical charts & graphs
Team Focused
- Status by engineer & test environment
- Recently failed test cases
- Linked application defects
Open source tools like Allure, ReportPortal and Extent Reports generate polished reports tailored to test automation needs. These assist both engineering and leadership in continuously improving automation solution.
9. Validate Across Real Browsers, Devices and Networks
Accurately replicating the diverse settings and constraints of real-world user environments is vital for comprehensive validation.
Simulators and emulators fail to account for variances like:
✖️ Inconsistent JavaScript Engines 💻
✖️ Low Memory Conditions 📱
✖️ Variable Network Speeds 📡
✖️ Specific Browser & OS Versions 🆚
Cloud testing platforms grant access to thousands of unique real mobile and desktop browser combinations. For example:
🔎 Chrome, Safari & Firefox on macOS Ventura
🔎 iOS 16 Safari on iPhone 14 Pro Max
🔎 Windows 11 Edge Browser on Surface Laptop
Configure these to match your application traffic for targeted test coverage. This boosts confidence prior to release by confirming seamless operation despite fragmented landscape.
10. Continually Execute Automated Checks
Running automation just during hardening leaves little time for addressing defects. By shifting testing left, issues surface much earlier:
🕑 On Every Code Commit – Runs specific build verification checks
🕑 Nightly Runs – Full regression pack across UI, APIs and databases
🕑 Weekly Production Testing – Confirm no fragmentation across live ecosystem
Time-based triggers through CI/CD pipelines at each stage socializes defects quicker and prevents release delays. Pair with virtual user load testing on staging environments for additional protection.
By adopting even a portion of these balanced test automation best practices, you will streamline test creation, strengthen reliability, and speed up pipelines. Each principle works together to drive efficient processes and expand visibility.
Now it‘s your turn to evaluate where your current strategy has opportunities for improvement and explore where these methods can help. I’m eager to hear your experiences and happy to offer additional advice as you progress your automation efforts!
