What are the latest software testing trends and how can you prepare for them?

AI/ML is the current imperative in the QA industry, especially to achieve 100% sprint automation. In today's dynamic landscape where application components frequently change, an automation framework must adapt seamlessly without manual intervention. Currently, QAautoMATER tool offers an AI-driven test solution. With just one click, it transforms manual test cases into automation testing. I highly recommend QAautoMATER if you seek an AI/ML-based testing solution.

Domain knowledge plays critical role in any application testing. Though we have AI / ML related tools like Functionaize still Domain knowledge is required to optimize tests (either manual / automation).

In the realm of testing, AI and ML are like friendly assistants, revolutionizing the process. Picture them automating test design, execution, and analysis, reducing errors and human effort. Imagine using tools like Appium, Selenium, Testim, and Mabl to embrace this transformation. These technologies enhance test coverage and speed, ensuring faster feedback and delivery. To adapt, learn the magic behind AI and ML algorithms, understanding how they can gracefully apply to diverse testing scenarios, creating a harmonious blend of efficiency and innovation.

AI and ML are revolutionizing software testing, automating processes and improving efficiency. They enhance test coverage, accuracy, and speed, enabling faster feedback and delivery. To adapt to this trend, learn AI and ML tools like Appium, Selenium, Testim, and Mabl. Understand how AI and ML algorithms work and apply them to different testing scenarios. This knowledge empowers you to leverage these technologies effectively, stay competitive, and achieve better testing outcomes. Continuous learning is essential in this rapidly evolving field.

ML y la AI aun siento que están lejos de ser un “estándar”, sin embargo, la facilidad con la que los cambios se dan en tecnología y las innovaciones que han surgido últimamente en especial con la inteligencia artificial generativa definitivamente cambiarán el juego de todos los testers. Entender profundamente ML y AI definitivamente serán clave en el futuro de la automatización de pruebas.

Shift left approaches have always some extra benefits as we call prevention is better than cure. Shift right approach can be useful in context of frequently changing use cases or adhoc scenarios where test data is highly unpredictable like IA tools development

If you want to shift-left, go further than the implementation. Test designs, artefacts, ideas, working methods, leadership, you name it. There's more to the left than what at first meets the eye. Also change the "shift" to "spread" and you'll do better. ;)

Following the shift-left approach effectively involves several key practices. Begin by establishing strong collaboration between development, testing, and other stakeholders from the project's outset. Encourage a culture of quality across the team and provide training on testing practices for developers. Employ techniques like static code analysis and early unit testing to catch defects at the earliest stages. Regularly review and refine testing strategies to adapt to evolving project requirements. Continuous communication and a commitment to quality at every stage contribute to a successful shift-left implementation.

Shift left mean also that testability is taken care early enough. Also TDD and ATDD can be included to shft left. Testing should be taken care in contracts and plans too.

An automation solution capable of generating pseudo code for test scripts based on design documents or manual test cases is crucial for inline integration with a shift-left approach. QAautoMATER stands out in this aspect, creating automation scripts with just one click. It efficiently transforms manual test cases into automation scripts, showcasing the unique capability to do so even before the application has been fully developed.

In my experience cloud based testing services/tools are easy to setup, comes with lesser installation and configuration headaches and sometimes your first test can be written in hours rather than days. But this flexibility comes with a multilevel cost of paying for Cloud provider and tool licenses. We don't have free/open source alternatives such as non cloud tools like Selenium, Cypress, PlayWright and many others. So it's more of a tradeoff in terms of your testing OKRs and if you can achieve better ROI when using a cloud based testing infra or a humble locally installed tool that can be good enough to achieve all your business goals. This comparison needs to be done before enterprise decides to invest in cloud tools.

Cloud-based testing is a powerful strategy that leverages the cloud's inherent advantages to elevate the software testing process. The scalability of cloud services ensures that resources can be adjusted according to the varying demands of the testing phase, making it highly cost-effective. It also offers unmatched flexibility by allowing tests to be conducted across a vast array of environments and devices without the need for physical infrastructure. With cloud-based testing, testers have the ability to simulate real-world user interactions across different locations, which significantly enhances test coverage and the overall quality of the product.

Shift gears: Move beyond "test the performance" to "engineer it!" Actively design, develop, and optimize for a seamless user experience. Holistic view: It's not just speed! Usability, reliability, scalability, and efficiency are critical components in the performance equation. Lifelong commitment: Embrace performance engineering throughout the software lifecycle, not just a final test phase. Toolbelt of possibilities: Load testing, stress testing, benchmarking, profiling, and root cause analysis are your allies in analyzing and improving performance. Mindset matters: Develop a performance-focused perspective. Ask "how fast, how smooth, how reliable?" for every design decision.

Performance Engineering ist ein ganzheitlicher Ansatz in der Softwareentwicklung, der darauf abzielt, Systemleistung und Effizienz von Anfang an zu berücksichtigen und zu optimieren. Es geht über traditionelles Performance-Testing hinaus, indem es sich auf die Analyse und Verbesserung von Systemarchitektur, Design und Implementierung konzentriert, um Leistungsprobleme pro-aktiv zu vermeiden. Performance Engineers arbeiten eng mit Entwicklern zusammen, um Leistungsanforderungen zu definieren, Leistungsengpässe zu identifizieren und Lösungen zu implementieren, die die Skalierbarkeit, Geschwindigkeit und Stabilität der Software verbessern.

Performance engineering is a holistic approach that embeds performance considerations into every phase of the software development lifecycle. It transcends traditional performance testing by aiming not merely to measure but to enhance system performance proactively. Usability, reliability, scalability, and efficiency are all critical factors that performance engineers must constantly evaluate to ensure the software can withstand real-world conditions. Developing a performance engineering mindset involves a proactive attitude towards system performance, with an emphasis on early identification and mitigation of potential issues.

Two less obvious options for UX testing are: 1. Giving the authority to your most junior QA's and recent hires to call out situations where using the product is frustrating or when they can't find a feature they're looking for, since more experienced QA's and users of the system are already "indoctrinated" and accustomed to the environment just by participating in its development and 2. Measuring the number of clicks/taps/commands that an average user needs to input before reaching various screens or functions they might be looking for and thinking whether the number indicates your system is "too deep", "too shallow" or just right.

User experience testing is essential to creating software that is not only functional but also delightful and intuitive for users. It focuses on understanding user interactions and ensuring the product aligns with their needs and preferences. Employing a variety of methods, from usability testing and surveys to interviews and journey mapping, provides a comprehensive view of the user experience. To adeptly conduct UX testing, it's important to grasp UX design principles, research methodologies, and evaluation practices. Utilizing tools like wireframes and prototypes helps visualize changes, while personas and heuristics facilitate a deeper connection with user behaviors and expectations.

1. Scriptless Test Automation 2. Robotic Process Automation (RPA) 3. Combined Automation & Manual Testing 4. API and Service Test Automation 5. More Data, Better Data 6. Performance Testing 7. Testing Centers for Quality 8. Cyber Security and Risk Compliance 9. IoT (Internet of Things) Testing 10. Mobile App Testing Automation 11. Infrastructure as Code (IaC) 12.Application Penetration Testing 13. Agile, DevOps & CI/CD 14.QAOps

Prepare a charter of which new technologies foster adoption in testing/Quality assurance area. Based on it, prepare a list of testing areas and corresponding tools. And including automation. Prepare an asset bank in each of the above based on the project type (if something needs to be built then can be planned for so). A front end testing differs from API testing vs performance testing that needs huge production replica data vs data testing which would need synthetic data generation. Once the above is in place, it can be enabled as a ‘Framework’ in place to be formulated as a COE practice.

Many companies would benefit going "back to basics". That is: not to wait for AI/ML/test automation to save them, but focus to competence development so that people (incl. leaderships and managers) 1) are aware of modern testing (they know what they need to do), and 2) are properly skilled (they know how to do the needed tasks).

Try not to get distracted by the latest trends... It should be less about the buzzwords and more about what you can truly utilize and get help from. Don't feel pressured to use any emerging technology if it doesn't solve a problem you have or if it requires too much energy to introduce.

Stay updated on trends like AI in testing, shift-left testing, and DevOps integration. Embrace automation for efficiency and speed. Focus on performance engineering to ensure applications meet user expectations. Enhance security testing to address evolving threats. Cultivate a culture of continuous learning to adapt to emerging trends in the dynamic field of software testing.