What are the latest tools and technologies that quality assurance professionals should learn?

the role of a QA professional is evolving, and it's crucial to keep pace with the changing landscape of software testing. Learning testing types and tools such as Selenium, Cucumber and Robot Framework, API testing tools ( postman or other tools), empowers QA professionals to contribute more effectively to the software development process. By following the suggested roadmap for skill development, QA professionals can enhance their capabilities and remain valuable assets in the industry.

In my experience, most companies are trying to adapt to the culture of having full stack and SDET. Keeping this in mind, QA needs to have knowledge of Python, React and other programming languages. This is in addition to the standard QA tools like Postman, swagger, automation tools like selenium, playwright and DevOps tools for CI/CD.

Getting started with test automation requires to follow certain steps - Choose a programming language commonly used in automation, such as Java, Python, C#, or JavaScript. - Research and explore popular automation testing tools. -Install the necessary development tools and software. For example, if you choose Selenium with Java, you'll need to install Java Development Kit (JDK), Eclipse or IntelliJ IDEA as your Integrated Development Environment (IDE), and the Selenium WebDriver. -Begin with a simple automation project to get hands-on experience -Understand the automation framework that you'll be using -Learn how to use assertions to verify that the application behaves as expected. -Stay updated with the latest trends

QA Tools are just that "tools", first you need to know WHAT you want to check - then HOW and just in the end set the best TOOL for the job at hand (not counting a specific language). That said, it is expected to know the requirements for each test and its most suitable Tool for running and tests management. The most common and latest tools, as I know them are - Jira, Postman, Cucumber, Python and more…

QA pros should learn: automation tools like Selenium, Cypress for web and Appium for mobile; test management with Jira or TestRail; performance testing using JMeter; AI-powered testing tools like Testim; cloud platforms like BrowserStack for device testing; and DevOps tools like Jenkins. Basic coding skills in Python or JavaScript are also helpful. Familiarity with Git for version control is important too.

However still there is more examples haven't seen to apply this tech in QA process and how QA person will be beneficial because of this?

To date (in my use), the AI has helped me at the code level and editing test documents. He is not yet at the level of writing tests for us himself especially because you need to really understand the system for that.

Gain knowledge in AI-based testing tools like Testim or Applitools for visual testing and anomaly detection. Learning the basics of machine learning models can help in predictive analytics and test result analysis.

This is not necessarily true. Of course, any knowledge of devops will help the automation developer to do more things himself. But there are many places where there is a devops team and the automation developer will almost never touch it.

Understand Jenkins for integration, Docker for containerization, and Kubernetes for orchestration. Learning about CI/CD pipelines and how testing fits into DevOps practices is crucial for seamless software delivery.

The definition is not so precise. These are not "cloud tests" but automatic tests that are carried out on the cloud (so that we don't have to buy computers/phones and run the tests at our place. We will note that lectures on the cloud often cost a lot of money.

Get acquainted with cloud platforms like AWS, Azure, or Google Cloud. Learning about cloud-based testing tools and services can help in performing scalable and flexible testing environments, accommodating load, performance, and security testing.

Agile testing aligns with agile software development, emphasizing collaboration, incremental testing, and adaptation. It fosters communication and feedback, improving product quality. Agile testing tools like Jira and Confluence support this process. It's crucial for delivering high-quality software incrementally and responding to changing requirements efficiently.

Understanding Agile methodologies and principles is key. Learn how to integrate testing into the Agile lifecycle, with tools like JIRA for project management, and Zephyr or qTest for test management, to ensure continuous feedback and iterative improvement.

Most of these tests will be performed by the UX team and the product team. Of course, if the QA person goes through an experience that he is not satisfied with, he will bring it up, but that is less of his responsibility.

Focus on learning tools like UserTesting or Lookback for real user feedback, and Hotjar or Crazy Egg for heatmapping and user behavior tracking. Understanding UX principles and accessibility standards is also vital for delivering user-centric applications.

It depends on the company, product or service, end users and potential users. If the most urgent is to try to increase end users volume than A-B testing tools. if the most urgent in ROI then analytics tools and in general then use AI tools to help specify the current needs.

Tools are useful. What's more important is knowing why, when, and how to use them. Also: acknowledging that test-related tools come in a variety of categories: test case management, planning, knowledge acquisition, tools to track and present metrics/KPIs, tools to track initiatives and discussions (to avoid institutional amnesia),... and testing tools. We tend to focus on the last one, but testing is only useful if it's part of an epistemic and action-oriented system: What do you do with what you've learned about your product through testing activities? Can you use this information to raise your team's conceptual maturity and technical focus? Improve inter-team dynamics? Does it help you test smarter? Make your work more meaningful? etc.

I don't exactly know why AI thinks IT Quality Assurance should be in the manufacturing section. Or that QA tools are only software development. But since there's no where I can go to provide feedback on this, I'm here writing this message. Nonetheless, Manufacturers could benefit from reading these articles, as some of the principles can be applied. Automation testing is the same as flying blind. You can place an unexperient professional to follow your work instructions for machine operations, quality checks. They should be able to follow through. AI and ML can simulate hundreds of scenarios of operations from assembly lines to soldering. Cloud testing is like offsite operations. Agile aligns well with Lean Six Sigma.

Stay updated with the latest in IoT testing for connected devices, performance testing tools like JMeter or LoadRunner, and security testing tools such as OWASP ZAP or Burp Suite. Additionally, understanding the basics of Blockchain testing and virtualization tools can broaden your testing capabilities. Continuous learning and adaptability to new technologies are key in the ever-evolving QA landscape.