Wie gehen Sie bei der Software-Qualitätssicherung für verschiedene Arten von Software vor, z. B. Web, Mobile oder KI?

In my view, Web QA also involves fostering a proactive quality culture within the development team, emphasizing collaboration and clear communication. It's crucial to engage early in the development process, outlining test cases based on requirements. Continuous integration and continuous delivery (CI/CD) pipelines can streamline testing and integration, ensuring quicker feedback loops. Additionally, monitoring and gathering user feedback post-deployment aids in identifying potential issues, enhancing the overall user experience. In essence, Web QA transcends mere testing—it's a comprehensive approach that intertwines with the entire development lifecycle.

While unit testing along with integration and system level testing is important for quality assurance, In my experience working with a B2B software platform, I have found that it is critical to understand the End-2-End user's flow and real-life use cases in order to provide a proper quality of the system.

1. Utilize manual testing to check for errors, bugs, and defects, and automated testing for repetitive/complex checks, ensuring web app/site standards. 2. Conduct cross-browser testing to verify consistent and correct functionality across different browsers. 3. Perform responsive testing to confirm the web software adapts seamlessly to varying screen sizes and resolutions on diverse devices.

Web software QA focuses on cross-browser compatibility, responsive design, and security testing. Key techniques include automated UI testing, load testing, and thorough validation of user inputs and data handling. Mobile QA emphasizes device fragmentation, network conditions, and battery efficiency. This involves extensive testing on multiple devices, simulating various network states, and monitoring resource usage. AI software QA requires rigorous testing of model accuracy, bias detection, and edge case handling. This often includes large-scale data validation, A/B testing of model versions, and careful monitoring of model drift over time.

The first 3 sentences are not unique for Web QA - they are general, about Software QA. Secondly, it would be nice to mention something about the web-based architecture: client-server, microservices, APIs, and how to ship and test them separately; about links and URLs or routes within the application; about network issues, and also about SEO and search engine compatibility. There are different delivery pipelines because you don't need to make app builds and users don't need to install apps on their devices. There are lots of different aspects of vulnerabilities and performance testing in comparison with, for example, mobile apps.

In comparison with web apps, it is crucial to remember some, more unique aspects of mobile apps, such as platform-specific tests, App Store compliance, offline features and apps' workability, device-specific testing, mobile hardware features, push notifications, touch gestures, permissions, updates, integration with a mobile device ecosystem, battery and resource usage, in-app purchases/payment processing, app accessibility. App security and app performance also have lots of differences with web-based apps, and you need to use different approaches to test them properly.

Approaching Mobile QA involves guaranteeing optimal performance, security, and user experience of mobile apps across diverse devices and OSes. I prioritize a comprehensive testing regimen encompassing functionality, usability, performance, compatibility, security, and localization. My perspective involves meticulous manual testing, where I meticulously execute test cases on real and simulated devices to identify bugs and defects. Additionally, I advocate for leveraging automated testing tools to efficiently conduct repetitive and intricate tests.

The approach to software quality assurance varies for web, mobile, and AI applications. For web apps, it involves extensive cross-browser and device testing, along with automated tests. Mobile apps require testing across different OS and devices, including real-device testing, and a focus on user experience. AI software testing focuses on machine learning model accuracy, reliability, and ethical considerations, with continuous monitoring and retraining. Continuous integration and deployment practices are beneficial for early issue detection, and collaboration between teams is essential for effective quality assurance across different software types.

To conduct thorough Mobile QA, focus on testing key aspects: functionality, usability, performance, compatibility, security, and localization. Use a mix of 5 devices: 4 Androids (high-end, mid-range, budget, and old) and 1 iOS, creating over 20 test combinations. This ensures comprehensive coverage across different network conditions, price ranges, and device ages. Also, test common permission levels including app permissions, payment transactions, resource management, and network connectivity. This approach ensures the app performs reliably under various user scenarios, maintaining privacy and security across diverse devices, and delivering a consistent user experience. This strategy is essential for validating app quality effectively.

Mobile QA is a dynamic field that requires a blend of meticulous attention to detail and creativity in test scenario design. With my background in both manual and automated testing, I recognize the importance of adapting to the unique challenges of mobile platforms. The diversity of devices and operating systems demands a robust testing strategy that ensures comprehensive coverage. As a QA Engineer, I've learned that leveraging automation can greatly enhance efficiency, but manual testing remains invaluable for assessing the nuanced user experience. Balancing these approaches is key to delivering high-quality mobile applications.

Ethical and contextual testing needs to be performed to ensure that a system operated in an ethical and responsible manner and does not discriminate against any specific group or individual and contextual testing performance on different contexts or scenarios to evaluate its performance and accuracy with real world scenarios. Behavioral Testing should be done with specific inputs and evaluating its output to ensure system behavior.

AI QA encompasses a strategic blend of traditional software testing methodologies and AI-specific considerations. It's crucial to meticulously examine not only the technical functionality and reliability of AI systems but also the underlying algorithms' integrity. Ethical dimensions like bias detection and mitigation are paramount. Personally, I emphasize a proactive approach, integrating testing early in the AI development lifecycle, and leveraging data diversity. Continuous monitoring and adaptation are key, given the evolving nature of AI technologies. Collaboration between domain experts, QA professionals, and data scientists is essential to ensure comprehensive AI QA that goes beyond surface-level performance assessment.

In AI QA, it's crucial to recognize that AI systems are dynamic and evolve with new data. As a QA Engineer with a background in manual and automated testing, including API and database testing, I understand the importance of continuous testing throughout the AI lifecycle. This means not only validating initial functionality but also ensuring ongoing performance as the AI system learns and adapts. It's about safeguarding the integrity of AI systems to make decisions that are not only accurate but also fair and ethical, which is paramount in a world increasingly reliant on AI-driven solutions.

- Data quality assurance: Ensure the quality, accuracy, and integrity of the data used to train and test AI models. - Model validation: Test the AI model's performance against diverse datasets, including edge cases and outliers, to assess its robustness and generalization capabilities. - Ethical and fairness testing: Evaluate the AI system for biases, discrimination, or unintended consequences that may impact different user groups unfairly. - Interpretability and explainability testing: Assess the AI model's transparency and ability to explain its decisions or predictions to users and stakeholders.

There is so much more than meets the eye. Adapting Software Quality Assurance (QA) approach to the specific needs of various software types is key to ensuring top-notch quality. From web and mobile applications to cutting-edge AI solutions, each comes with its set of challenges. It's so important to take a holistic approach that is system wide and systematic. 🚀 #SoftwareQuality #Innovation #QAstrategies

1. Universal QA Practices: Regardless of software type, maintaining a clean and well-managed codebase, integrating continuous integration/continuous deployment (CI/CD) processes, and promoting a culture of quality across the development team are critical. 2. Adaptive Strategies: Develop a QA strategy that adapts to new technologies and methodologies, staying current with industry trends and tools to handle emerging challenges effectively.

In software quality assurance: Web Applications: Emphasize testing across browsers/devices, ensuring compatibility, security, and responsiveness. Automation is valuable for repetitive tasks and load testing. Mobile Applications: Test on diverse devices and OS versions, focusing on functionality, usability, and performance. Adhere to platform-specific guidelines for submission. AI Applications:Prioritize testing AI algorithms for accuracy, reliability, and robustness. Validate performance with diverse datasets, monitor for biases, and assess responses to unexpected inputs.

I suspect this article was generated by AI or someone greatly lacking fundamental knowledge around Quality Engineering. It’s propagating misinformation about the work of Quality Engineering and make no mention of Risk assessment, the root goal of our work. It needs to be removed or revisited based on a Quality Experts input and review.

It seems like there are several important aspects missing from this article, and each section could be expanded into separate articles to provide more detailed guidance on how to achieve them. Finally, when approaching software quality assurance for different types of software, it's crucial to develop a customised strategy that takes into account the specific characteristics and requirements of each platform. This includes addressing fundamental concepts such as data privacy concerns for mobile apps, considerations of bias and explainability for AI, and identifying potential vulnerabilities in web applications.