QA2.0:AI-Integrated Test Automation Tool Evaluation Criteria
World’s First AI-powered test automation tool evaluation guide.
If you haven’t checked my previous articles, I recommend you start from the index page to understand the full context.
AI-Integrated Test Automation Tool Consideration and Classification article
Why different criteria for AI-powered tools?
Why can’t we use old-school test automation tool evaluation criteria to evaluate AI-powered? That might be the first question that comes into your mind. So let’s discuss why first.
Implementing a test automation framework using a programming language (Java/C#/Python) and other framework integrations (JUnit/TestNG/Selenium) requires a great deal of technical knowledge.
For example, if you are implementing a simple framework for web automation, you will need to know:
- Java or any other programming language
- Test executors (JUnit/TestNG)
- Selenium
- Build tools (Maven/Gradle etc)
- Version controlling (GIT)
- IDE (Eclipse or IntelliJ IDEA)
- Reporting frameworks
- Knowledge of BDD (if adding the BDD layer)
This list goes on based on the complexity of your application.
Even though you may not have realized it previously, you are an expert at the majority of the skills. :)
However, when we consider most of the AI-Integrated tools story is different. Most of them don’t require any technical knowledge.
So previously to do a successful automation
QA methodologies + Technical knowledge of frameworks and programming languages + Domain knowledge
But now
QA methodologies +T̶̶e̶̶c̶̶h̶̶n̶̶i̶̶c̶̶a̶̶l̶̶ k̶̶n̶̶o̶̶w̶̶l̶̶e̶̶d̶̶g̶̶e̶̶ o̶̶f̶̶ f̶r̶a̶m̶e̶w̶o̶r̶k̶s̶ a̶n̶d̶ p̶r̶o̶g̶r̶a̶m̶m̶i̶n̶g̶ l̶a̶n̶g̶u̶a̶g̶e̶s̶ + Domain knowledge
So, we have to make some adjustments to the automation tool evaluation criteria.
The main considerations in selecting an AI-powered tool or platform
The key Criteria in the tool evaluation :
🔍 Compatibility and Integration
🤖 AI Capabilities
📜 Test Script Generation and Maintenance
🌐 Usability and Learning Curve
⚙️ Scalability
🌐 Cross-Browser and Cross-Platform Support
📊 Reporting and Analytics
🌐 Community and Support
🛡️ Security
💰 Cost and Licensing
🔄 CI/CD Support
🌟 Vendor evaluation
Let’s discuss the above points in more detail manner.
Compatibility and Integration:
Make sure that the tool's integration into your development and testing environment is smooth. The software for development should match up with the programming languages, frameworks or technologies you employ on your projects.
This might not be valid if you go with E2E automation platform which provides test case writing to CI/CD support.
AI Capabilities:
Assess how advanced is the level of the tool’s artificial intelligence. Focus on technologies like natural language processes, machine learning, predictive analytics etc., which will assist in enhancing the processes of automation and decision making.
Test Script Generation and Maintenance:
Evaluate how the tool is capable of developing and updating test scripts without human assistance. An effective AI-integrated tool should respond to modifications occurring in the application, which may include its user interface or operation procedure, thereby decreasing the manual efforts required to produce scripts and updates.
Usability and Learning Curve:
Also, the ease of operation and its learning curve should be considered too. A user-friendly interface and comprehensive documentation can contribute to faster adoption and implementation within your testing team.
Scalability:
Make sure the tool can adjust to meet the future demands of your projects. This should cope with a large amount of test procedures, different operating conditions as well as growing community support.
Cross-Browser and Cross-Platform Support:
Ascertain if the tool enables browser and platform-driven testing. This ensures that applications run consistently in any environment.
Cross-browser support is only applicable for UI testing.
Reporting and Analytics:
Choose tools that come with in-depth and actionable reports. Continuous improvement requires analysis of test results, tracking of KPIs and meaningful insights generation.
Community and Support:
Take into account the scale and the level of activity of the tool’s user community. A strong community often indicates active development, a wealth of resources, and community-driven support. The best place to look for is the Stackoverflow community.
Additionally, check for the availability of professional support options and ETA for issue resolution. Also the support team distribution. You might have to wait for a day to get a reply if the support team is in a different time zone.
Security:
Ensure you select the tools that observe strict security policies because of the sensitivity associated with software development and analysis. Secure data storage, encryption, as well as adherence to relevant industry standards and rules.
Cost and Licensing:
Calculate the total cost of ownership (TCO) that includes not only the licensing fees but also support costs and other related expenditures. Examine if the tool is worth the price it commands by improving productivity and easing workload.
Continuous Integration and Continuous Deployment (CI/CD) Support:
Ensure there is a smooth integration of the tool within CI/CD pipelines. It plays a big role in including automated testing in the development process and quick and trustworthy software releases.
Vendor Evaluation:
Research the reputation of the tool’s vendor. Consider factors such as the vendor’s history, customer testimonials, and case studies. A reputable vendor is more likely to provide reliable and well-supported tools. Also, remember to check their product roadmap.
More detailed tool analysis
Conclusion
Nowadays, most companies are claiming they are using some kind of AI. That’s why I have started QA2.0 tool review article series to give you unbiased feedback.
Remember, most of these tools are in the early stage of AI-Integration. So they can’t do magic.
So our responsibility is to find the best available tool and make our life easier.
