QASource Blog

Hyperautomation 2.0 emerges as a transformative force, redefining traditional testing methodologies. It aims to automate end-to-end validation processes using core AI technologies and address challenges such as daily release cycles.

Localization testing (L10n testing) extends beyond translation to ensure your software application can seamlessly adapt to various languages, regions, and cultural nuances. This meticulous process guarantees that your software functions smoothly across languages while respecting diverse user bases' unique customs and preferences. But manual localization testing is no simple task. Integrating AI and automation into the localization testing process enhances efficiency and ensures precision— vital for delivering a top-notch global user experience.

Explore IoT security testing in this newsletter, covering landscape insights, testing importance, AI-driven use cases, dedicated tools, and best practices. Emphasizing the critical need to secure interconnected IoT systems in today's digital age.

Unlock the potential of AWS Generative AI in DevOps. Experience streamlined code development, improved collaboration, and optimized CI/CD. AWS's cutting-edge AI simplifies and enhances DevOps workflows, making them more efficient.

Legacy code is a common challenge many organizations face when dealing with outdated software systems. As technology evolves rapidly, businesses must keep their tech stacks up to date to remain competitive. Converting codes to a modern tech stack can be complex and time-consuming, but AI makes it easy and efficient. This blog covers how AI can streamline the legacy code conversion to the latest tech stack, providing numerous advantages such as efficiency, accuracy, savings, and risk reduction.

Voice recognition and voice assistants have become central to the software as a service (SaaS) industry, changing how we interact with SaaS and promising benefits for businesses and users alike. But if you plan to offer a voice assistant or other voice recognition tool, it’s important to thoroughly test your offering before bringing it to market.

Analysts predict AI's market size in software testing will reach an impressive $4 billion by 2024. But the impact of all this AI is tied to the precision and robustness of the underlying models.

AI in mobile testing is booming, pushing developers to stay competitive and enhancing regression testing, script, data, performance, usability, and visual testing.

Micro-services architecture excels in complex, large-scale apps with diverse tech needs and evolving features. Performance testing is crucial for smooth interactions, scalability, bottleneck detection, and reliability. This series explores AI-driven techniques for assessing efficiency and system performance in micro-services architecture.