The future of MXDPs is bright. They are becoming increasingly popular as the need for them grows. As new devices and modalities emerge, so too will new platforms to support them. We can expect to see more platforms that offer low-code and no-code development, as well as platforms that focus on specific modalities or devices.

Multi-experience testing in software development refers to the testing of applications or systems that are designed to provide a consistent user experience across multiple devices and interaction modes. This includes testing the software on various platforms such as web, mobile, wearables, and other emerging interfaces like voice and chatbots.
Here are some key points about multi-experience testing:

• Cross-Platform Testing:  Multi-experience testing involves ensuring that the software functions correctly and looks good on different devices and operating systems. This includes testing on various browsers, mobile devices (iOS, Android), wearables, and other smart devices.
• Multi-Modal Testing:  Applications that support multiple interaction modes such as touch, voice, gestures, etc., require testing to ensure that each mode works as intended. This includes testing the user interface, responsiveness to different inputs, and overall user experience.
• Usability Testing:  Usability testing is an important aspect of multi-experience testing. It involves evaluating how easy it is for users to interact with the software across different platforms and devices. This can include testing for accessibility, readability, navigation, and overall user satisfaction.
• Performance Testing:  Multi-experience testing also involves performance testing to ensure that the software performs well across different devices and platforms. This includes testing for load times, responsiveness, and overall system performance under different conditions.
• Security Testing:  Security is a critical aspect of multi-experience testing. It involves testing the application for vulnerabilities across different platforms and devices to ensure that user data is protected and the software is secure from potential threats.
• Automation:  Given the complexity of multi-experience testing, automation plays a crucial role in ensuring efficient and effective testing across various platforms and devices. Automation tools can help streamline the testing process and improve test coverage.
  Overall, multi-experience testing is essential for ensuring that software applications provide a seamless and consistent user experience across different devices and interaction modes. By thoroughly testing the software across various platforms, devices, and modes, developers can identify and address potential issues before they impact end-users.

We will create a bdd scenario and then we will discuss how to generate test code by using nlp techniques

People will try to generate test code by using nlp, they will learn nlp techniques.

Some common pitfalls companies fall into while implementing test automation systems and some suggestions to avoid them.

Keeping these pitfalls in mind when implementing test automation can save you so much time and effort.

Manual end user test case scenarios are fully automated and operated by Automation Team in Airties. The aim is to take the desired software to Alpha phase after the successful runs of the test suites. Our user experience tests are run in real houses with real wireless clients and with a robot that we designed.

• Fully automated test plans without manual steps in real environment.
• Easy and fast delivery to market.

Artificial Intelligence (AI) is rapidly changing the landscape of software testing, driving efficiency, accuracy, and innovation. This talk will explore how AI-powered tools and techniques are reshaping traditional testing processes, making them faster, smarter, and more reliable. Key topics will include AI’s role in automating repetitive tasks, enhancing test coverage, improving defect detection through machine learning algorithms, and predicting potential issues before they occur. Attendees will gain insights into real-world applications of AI in testing, including case studies and best practices, and learn how to integrate AI effectively into their own test automation workflows to maximize productivity and reduce time-to-market.

We must learn to work with AI. If we do not learn to work with AI, our jobs and expertise may be in danger in the near future.

In this talk, we will explore the transformative power of Hyperautomation in test automation, focusing on how AI, ML, RPA, and BPM technologies are revolutionizing the testing lifecycle. Hyperautomation goes beyond traditional automation by introducing intelligent, self-learning systems that automate not just the execution of tests, but the entire process, including decision-making, test case generation, and continuous optimization. Through real-world examples and case studies from industry leaders like Amazon and Google, we’ll examine how Hyperautomation is being applied today and how it will evolve in the future to meet the growing complexity of software development.

The session will also cover practical steps for implementing Hyperautomation, including overcoming challenges like technology integration, scalability, and resistance to change. By the end of this presentation, attendees will have a clear understanding of how to adopt and scale Hyperautomation in their own testing environments.

• Understanding Hyperautomation: Learn what Hyperautomation is and how it integrates AI, ML, RPA, and BPM to create smarter, more efficient test automation systems.
• Real-world Applications: Discover how leading companies like Amazon and Google are leveraging Hyperautomation to automate complex test environments and improve software quality and release speed.
• Practical Implementation Tips: Gain actionable insights on how to get started with Hyperautomation in your organization, including strategies for addressing scalability, technology integration, and change management challenges.
• Future of Test Automation: Explore the future possibilities of Hyperautomation, from self-learning systems and AI-driven test case generation to fully autonomous testing processes that require little to no human intervention.