Automation Testing Tools in 2023: An In-Depth Guide to Code-based, Robotics & AI Tools

Test automation has become mission-critical for software teams aiming to deliver high quality applications at speed. The latest World Quality Report by Capgemini found that test automation rates have doubled over the past 5 years to reach 30% of all tests in 2021. And this growth shows no signs of slowing down.

Gartner predicts that half of all testing activities will leverage automation by 2025. With the demand for faster delivery of digital experiences, automation is now a competitive necessity.

Why Every Team Needs Test Automation

Let‘s first understand the key benefits driving increased adoption of automation testing:

1. Faster testing cycles: Automated tests execute much faster than manual testing. A test suite that takes hours or days can run in minutes.

2. Improved test coverage: Automation makes it practical to run thousands of test iterations with more data combinations.

3. Reliable regression testing: Automated tests can be scheduled to run on every commit or release to detect regressions quickly.

4. Consistent results: Automated tests perform precisely the same steps every time for consistent and reliable outcomes.

5. Early defect detection: Issues can be flagged earlier in the development cycle when they are cheaper to fix.

6. Improved efficiency: Automation reduces the time spent on repetitive manual testing activities.

According to a survey of IT leaders by Testim, 92% say test automation improved software quality. 85% reported faster release cycles enabled by automation.

Types of Test Automation Tools

Now let‘s examine the key categories of test automation tools available and how they differ.

Script-based Test Automation Tools

Script-based tools allow you to write code or scripts to execute automated test cases. They provide the most flexibility and customization capability compared to other approaches.

Code-based Test Automation Tools

Code-based tools support writing test scripts using programming languages like Java, Python, C#, Ruby, etc. Testers can write custom scripts from scratch to automate any test scenario or test data.

Selenium and Appium are the most widely used open-source tools in this category, often used for:

• Web app testing: Browser based testing across websites and web applications
• Mobile testing: Automating tests across Android and iOS devices
• API testing: Automating API tests cases and assertions
• Cross-browser testing: Running test suites across browsers like Chrome, Firefox, Safari

Key capabilities:

• Support for multiple languages like Java, Python, C#, JavaScript, Ruby, PHP
• Highly customizable tests through coding
• Access to native mobile app elements for robust mobile testing
• Open source frameworks with large user communities

Who uses code-based tools?

These tools require scripting expertise so they are used by expert test automation engineers and SDETs (software developer engineers in test).

Low-code Test Automation Tools

Low-code tools provide a graphical no-code interface and pre-built functions to help build test automation without hardcore coding. Some knowledge of scripting is still required for advanced customizations.

Key features:

• Drag and drop editor to build test flows
• Libraries of reusable test components
• Built-in support for commonly tested technologies
• Minimal coding required

Sample use cases:

• Web application testing across different browsers and versions
• Responsive web testing across multiple devices
• API testing with test data management
• Cross-browser compatibility testing
• Automated regression test suites

Who uses low-code tools?

Business analysts and test automation engineers who need to build automation without deep scripting expertise. These tools open up automation to non-developers.

Examples of popular low-code test automation tools:

• Ranorex
• Tricentis Tosca
• Worksoft Certify
• SmartBear TestComplete

No-code Test Automation Tools

No-code test automation tools allow even non-technical users to build automated test cases through a graphical "click-and-drag" interface without needing to write any scripts.

Key features:

• Visual model-based design
• No programming required
• Reusable components
• Integration with third-party testing tools
• CI/CD integration

Sample use cases:

• Web and mobile UI testing
• Cross-device and cross-browser testing
• Responsive web testing
• Workflow and integration testing
• omni-channel customer journey testing

Who uses no-code tools?

Business users, manual testers, QA analysts, SDET‘s. No-code tools democratize test automation across the organization.

Top no-code test automation tools:

• Testim
• Katalon Studio
• TestCraft
• Functionize
• SwagLabs

Robotic Test Automation Tools

Robotic Process Automation (RPA) tools simulate user actions via software robots and are being leveraged in test automation as well. RPA bots can interact with any UI and perform repetitive tasks to automate common test scenarios.

Key features:

• Bot recorder to capture user workflows
• Computer vision and machine learning to identify UI elements
• Central scheduling and orchestration of bots
• Detailed execution logs and reporting

Sample use cases:

• Data validation testing
• Field functionality testing
• Proof of concept prototyping
• UI responsiveness testing
• Daily sanity testing

RPA bots help take the robotic out of robotic test automation through human-like interactions. They are well-suited for simple, repetitive UI testing.

Examples of RPA tools with test automation capabilities:

• UiPath
• Blue Prism
• WorkFusion RPA Express

AI-enabled Test Automation Tools

AI and ML techniques are enabling more intelligent automation by mimicking human-like judgement in testing. AI capabilities are being incorporated into leading test automation solutions.

Key features

• ML-based test case suggestions based on changes
• Automated test maintenance without scripts
• Anomaly detection in test results
• Natural language test scripts and reports

Sample use cases:

• Optimizing test case design
• Predicting test coverage needs
• Auto-healing of broken tests
• Automating test reporting

By continuously training algorithms on testing data, AI can significantly enhance automation efficiency, coverage and accuracy.

Examples of AI-enabled testing tools:

• Functionize
• Applitools
• Sealights
• test.ai
• AutonomIQ

Comparison of Tool Types

Key Considerations for Selecting an Automation Tool

With a wide variety of test automation tools available, how do you select the right solution for your needs? Here is a step-by-step process for evaluation and selection:

1. Identify your primary automation needs

First assess your initial automation needs. What types of tests do you want to start automating – UI, API, performance, integration, security? This will help filter tools that support your test types.

Prioritize which test scenarios would help accelerate development cycles and improve quality.

2. Understand your team’s skillsets

Review the scripting and programming expertise within your team. This will determine whether you need a codeless, low-code or code-based test automation tool.

3. Involve testers and developers

Get hands-on feedback from both dev and QA staff to assess usability and match skill sets.

4. Assess customization needs

Determine the level of customization needed – do you need to parameterize a lot of test data or build custom libraries?

5. Review built-in integrations

Look for tools that integrate easily with your defect tracking, CI/CD and test management tools for maximum leverage.

6. Compare licensing models

Factor in both initial and ongoing licensing costs for paid tools. Open source options can offer flexibility and community support.

7. Don’t overlook support

Evaluate the vendor’s level of support, documentation and community forums to ensure a smooth experience.

Building An Effective Test Automation Strategy

Choosing the right automation tool is just the first step. You also need to develop an effective approach to maximize the benefits of intelligent test automation.

Here are 8 expert tips to build a successful automation strategy:

Start small, deliver value – Begin with pilot projects on high priority test scenarios to establish ROI. Then expand scope.

Involve developers early – Get dev team buy-in to share responsibility and create automation-friendly code.

Build reusable libraries – Create shared object repositories, utility functions, and templates to reuse across projects.

Leverage AI capabilities – Augment automation with AI-driven test creation, maintenance and analytics.

Integrate with CI/CD – Trigger automation as part of your Continuous Integration / Deployment pipelines.

Trace to requirements – Link automated test results back to underlying requirements for complete traceability.

Monitor rigorously – Track usage, failure rates, maintenance metrics to optimize ROI.

Keep improving – Refactor scripts, upgrade tooling, enhance reporting to continually improve.

Real-World Examples of Test Automation Success

Let‘s look at two examples of companies succeeding with intelligent test automation:

1. KUKA

KUKA AG makes industrial robots and automation systems. As they scaled their software engineering group, manual testing became slow and costly.

By implementing codeless test automation using Tricentis Tosca, they were able to:
[1]

• Shift left on testing, starting earlier in development
• Expand test coverage from 20% to 80%
• Reduce testing time by 95%
• Accelerate releases from 6 months to 6 weeks

2. Travelopia

Specialist travel group Travelopia operates over 50 travel brands globally. With a complex technology footprint, they needed to accelerate and improve testing across 200+ systems.

By adopting AI-driven automation with AutonomIQ, they achieved: [2]

• 50% faster test creation
• 90% reduction in test maintenance effort
• 20% increase in test coverage
• 10x improvement in rapid test creation for urgent scenarios

Key Takeaways

Here are the top highlights from this guide to getting started with test automation in 2023:

• Automation is becoming critical to gain speed and confidence in delivering high quality digital experiences.
• Code-based tools provide the most flexibility for expert coders while no-code tools expand automation to non-technical users.
• Look for tools that offer AI-enabled auto-healing, maintenance and analytics to enhance productivity.
• Develop a holistic automation strategy beyond just tools – involving processes, people and integrations.
• Monitor usage and metrics closely to showcase ROI and continuously improve.

The latest innovations in AI-driven automation are making testing faster, smarter and more reliable than ever before. By combining the right tools with testing best practices, organizations can release better applications in less time.

The future of quality is automation. The time to embrace intelligent test automation is now.