Testing software effectively has always been a challenging yet essential task for delivering high-quality digital products and experiences. As development cycles accelerate and systems grow more complex, manual testing struggles to keep pace. Automated testing provides a solution – executing pre-scripted test cases faster, more accurately, and at scale.
In 2023, maturing capabilities in artificial intelligence (AI) and machine learning (ML) will take test automation to the next level. As leaders in quality assurance and test engineering, we must understand these innovations along with proven approaches to harness automation and transform how we verify software.
This comprehensive guide examines test automation in depth, from types and frameworks through to best practices and emerging AI assistance. Master these techniques to improve release velocity, customer satisfaction, and outcomes in our app-driven world.
The Fundamentals: Manual Testing vs. Automated
Before exploring automation further, let’s review manual testing and why supplementing it with automation is so valuable.
The Manual Approach
Manual testing relies solely on human testers to verify software functions as expected. Testers develop test cases based on requirements, manually execute them across builds, record results, report bugs, and confirm fixes.
Prominent manual techniques include:
- Black box testing: Validating external behaviors without internal logic awareness
- White box testing: Verifying inner workings with access to code and architectures
- Exploratory testing: Dynamically designing and executing tests for new insights
While manual testing enables evaluation of subjective qualities like usability, solely relying on humans has downsides:
- Slow speed: Manual tests take time to design and execute.
- Prone to errors: Humans naturally make oversights running repetitive tests.
- Limited coverage: Feasible to cover only so many test cases manually.
- Difficult verification: Hard to verify fixes of complex bugs manually.
- No reuse: Each manual test execution starts from scratch.
Industry testing surveys indicate that on average, manual testers can execute 5-10 test cases per day depending on complexity.
The Promise of Test Automation
Test automation addresses these pitfalls by using specialized tools to execute pre-scripted test cases automatically. Popular tools include Selenium, Appium, TestComplete, and more.
Once tests are automated, they:
- Run rapidly: Tests execute as quickly as underlying systems allow. Automated tests achieve upwards of 70% faster execution than manual verification.
- Minimize mistakes: Tests perform precisely the same actions consistently without human error.
- Expand coverage: Automated parallel execution facilitates more test scenarios.
- Simplify verification: Automatically re-running tests checks fixes.
- Encourage reuse: Automated tests form a library easily rerun.
This amplifies tester productivity tremendously. A recent industry report found that on average automated tests can execute over 500% more test cases per day than feasible manually.
By combining manual and automated testing strengths, quality assurance teams unlock huge potential. Let‘s examine popular testing applications for automation.
Types of Tests to Automate
With a primer on the promise of test automation, which test types make the most sense to automate?
Functional Test Automation
Functional tests validate expected functionality and behaviors from an end user perspective. These lend themselves well to automation since multiple data sets can rapidly simulate real-world use.
Examples:
- Purchase checkout flows
- Form field validation
- UI flows across devices
- Business logic checks
Automating functional regression suites delivers confidence in release quality by automatically verifying critical user journeys every build.
Non-Functional Test Automation
Non-functional tests confirm more foundational system qualities like reliability, security, and scalability. Automating these recurring system-level checks brings consistency.
Examples:
- Load and performance profiling under utilization
- Disaster recovery checks for uptime
- Vulnerability scanning for security risks
By codifying non-functional requirements into automated test suites, organizations gain assurance quality attributes meet expectations every build.
API Test Automation
APIs serve as the glue integrating modern applications and services. Automated API testing verifies critical integration points programmatically without wide user interface layers.
Examples:
- Invalid input handling
- Edge case management
- Performance standards under load
API test automation acts as a safety net for potential breaking changes during rapid service development while enough UI tests cover critical user journeys.
Additional Test Types
While less frequent, automation applies to other testing needs including:
- Accessibility validation confirming compliance with disabilities standards
- Localization verification across languages and regions
- Compliance checks on meeting regulatory requirements
Understanding automation opportunities across test types is the first step toward harnessing benefits. Next let‘s explore popular test frameworks.
Top Automation Testing Frameworks
With test automation opportunities mapped, what frameworks bring structure to authoring and executing automated checks? Leading open-source options include:
Selenium
As the most popular web app test automation framework, Selenium supports multiple languages and browsers via a browser driver.
Key Highlights:
- Open source with abundant community resources
- Supports cross-browser testing
- Java bindings with Selenium WebDriver API
Appium
Appium facilitates test automation across native, hybrid, and mobile web apps. Using mobile OS automation frameworks, Appium operates apps similarly to actual user interactions.
Key Highlights:
- Supports iOS, Android, and Windows apps
- Leverages native app frameworks under the hood
- API supports Java, Python, JavaScript, C#, and Ruby
Cucumber
Cucumber employs simple language parsing to enable collaboration in authoring automated checks. Tests written in plain language drive automated test case execution.
Key Highlights:
- Features Gherkin business readable language
- Fosters collaboration across roles
- Integrates with Selenium and Appium
In addition to open source options, commercial tools like Tricentis Tosca and Parasoft SOAtest provide turnkey capabilities.
Framework Selection Considerations
Keep several factors in mind when selecting automation frameworks:
- Application types: Web, mobile, API, etc.
- Team skills: Languages known and scalability
- Reporting needs: Results display and customization options
- Tool ecosystem: Integrations with defect trackers and CI/CD pipelines
Take stock of your existing landscape before diving into a new framework. Next let‘s explore 5 leading benefits automation unlocks.
5 Benefits of Test Automation
With test types and frameworks background, let’s explore 5 leading benefits automation introduces:
1. Accelerated Feedback Cycles
Automating time-intensive test cases provides rapid feedback into the development process. Tests execute in parallel while developers work allowing designers and product managers to incorporate findings faster.
Industry data finds that teams with extensive test automation verify fixes over 37% faster than traditional manual testing environments.
2. Increased Testing Coverage
Manual testing covers fraction of possible test cases due to human limitations. Automated execution facilitates running a wider variety of scenario combinations increasing coverage.
One survey discovered that on average, automation enables executing 9 times more test cases overall compared to purely manual testing.
3. Improved Accuracy
Humans naturally make oversights executing mundane, repetitive testing tasks. Automated tests perform precisely the same steps reliably improving accuracy.
Automation achieves upwards of 99% test execution accuracy whereas industry averages for manual testing hover in the 92-95% range depending on case complexity.
4. Enhanced Insights
Test automation generates rich quantities of structured test data. Analyzing results exposes insights into usage patterns, performance issues, and other phenomena.
For example, running an automated 5-user load test nightly could uncover stability regressions invisible under normal use.
5. Cost and Time Savings
Although automation requires upfront investment, multiplying the efficiency of human testers saves significant time and cost long-term. Maintenance also becomes easier over time through test re-use.
Research suggests comprehensive test automation at scale drives 30-50% reductions in budgets and lead times over 3 years.
Reaping these rewards does necessitate overcoming a few common hurdles however.
Top Challenges in Test Automation Adoption
While highly beneficial, test automation does present adoption obstacles software teams must address:
1. Initial Tooling Investments
Purchasing automation software and configuring environments demands upfront budget allocation. For on-premise tools this includes test lab devices, servers, tool licenses and more.
However costs are quickly recovered – one case study saw a midsize company regain their ~$120k automation investment in less than 5 months through enhanced productivity.
2. Test Maintenance Overhead
Like any code, automated checks require ongoing changes as applications evolve. Deliberate design for reusability and frameworks minimizing updates reduce this burden.
Research shows that without sufficient planning, test maintenance can consume over 50% of automation related efforts.
3. Talent Shortages
Sophisticated automation tools require technical skills for customization and upkeep. Cross-training testers or hiring specialized automation engineers is key to success.
Demand for skilled test automation talent outstrips supply by over 4x with nearly 500,000 open roles expected by 2025.
Anticipating these adoption challenges ensures you account for them in planning and processes. Let‘s explore best practices implementation next.
6 Steps for Successful Test Automation Implementation
With potential hurdles understood, follow these best practices introducing automation:
Step 1: Identify Automation Candidates
Audit existing test cases and requirements determining which maximize value and feasibility to automate based on:
- Execution frequency – Regularly run tests
- Business criticality – Revenue impact of defects
- Flakiness – Tests generating false failures
- Duration – Time-consuming manual checks
Also consider areas that automation enables better than manual – multi-data set, cross platform etc.
Step 2: Select Automation Tools
Research automation tools like Selenium and Appium that align to application types, team skills, and reporting needs. Leverage free trials to prove out tools before purchasing.
When comparing automation tools, consider integration, scalability, maintenance needs and more along dimensions like:
| Dimension | Factors |
| Application fit | Web, mobile, API testing strengths |
| Adaptability | Scripting languages, frameworks, customization options |
| Execution | Test parallelization & environment support |
Step 3: Design for Reuse
Architect test scripts, functions, data stores, and frameworks promoting reuse across test cases. Apply patterns like keyword-driven testing facilitating easy maintenance.
Frameworks like Cucumber encourage designing automated checks that map closer to actual business requirements. This increases relevancy as needs change.
Step 4: Validate Early, Iterate Often
Start automating a small number of test cases first. Add more over multiple iterations while optimizing frameworks. Managing scope prevents overinvestment as maturity builds.
Industry data suggests that nearly 50% of automation initiatives fail in the first year due to technical debt accrued too early.
Step 5: Balance Automated and Manual
Strike balances relying on both manual exploratory testing and automated validation. Choose right technique for each testing need.
Testing experts believe at least 25% of critical test cases should remain manual to assess complex UX flows, edge cases, and more uniquely human judgement.
Step 6: Monitor and Optimize
Track test metrics like pass %, failures, flakiness, and runtimes. Investigation framework issues, test gaps, timeouts and more based on data. Continuously improve suite efficiency.
Adjusting processes while scaling automation prevents common missteps. Next let‘s cover core best practices.
Test Automation Best Practices
Beyond implementation steps, overarching automation best practices drive quality, efficiency and outcomes:
Maintain Clean Test Data
Structure test data in dedicated databases isolated from production systems. Seeding controlled datasets prevents unexpected changes compromising tests.
Experts recommend housing test data in cloud datastores independent from target applications to minimize collision risks.
Distribute Execution
Distribute test execution across multiple environments to avoid hardware bottlenecks and catch environment specific issues.
Running the same 1000 case regression suite across only 1 server creates unnecessary timing dependencies versus spreading over 5 VMs.
Fail Fast By Design
Architect tests to fail immediately on detecting issues versus running fully. Timely failures accelerate investigation and resolution.
Applying patterns like dependency injection assists forcing fast failures – if login fails, instantly halt vs. testing across all features.
Prioritize API Testing
Browser automated testing runs significantly slower than API checks everything else being equal. Favor direct API testing where possible.
Google research found API test automation executes on average 148% faster than full UI automation with one cloud provider.
Adopting these guidelines optimizes automated testing capabilities for the long haul.
Emerging Innovations: AI & ML in Test Automation
While proven frameworks drive test automation today, artificial intelligence advances are primed to transform testing in the years ahead.
ML-Driven Test Case Generation
Machine learning models show early promise automatically generating test cases from requirements and previous cases to expand coverage.
One academic study achieved 63% pass rate on an open source ML model automatically generating test cases versus human authored baseline.
AI-Enhanced Gap Analysis
AI promises to compare documented requirements versus deployed features identifying gaps for testers to address proactively.
Microsoft researchers recently unveiled a model accurately identifying requirement gaps with nearly 80% precision outperforming traditional manual gap analyses.
Automated Root Cause Identification
Analyzing test failures, ML classification algorithms can statistically pinpoint probable root causes saving debugging cycles.
Models may identify that a payment transaction failure occurred do to flagging fraud checks versus actual app crash based on training patterns.
Hyper-Personalized Reporting
Generative AI makes customized test reporting tailored individual preferences a reality delivering insights rapidly.
Subscribing executives may receive automated test results summaries in formats meeting their consumption needs.
These innovations only scratch the surface of AI’s potential augmenting test automation into the future. Monitor tooling breakthroughs as research translates into production-ready capabilities or contact our advisory team for a deeper discussion.
For now, focus on honing rigorous test automation capabilities powered by the proven frameworks covered here. Combining fundamentals with an eye to the future will enable teams confidently deliver higher quality software at DevOps speeds – an imperative for digital businesses.