Tag: software testing

In the rapidly evolving world of software development, the adoption of Gen AI is increasingly prevalent, revolutionizing each step in the life cycle. This is especially true in software testing. In this comprehensive guide, we will dive into the role of Gen AI in software testing, exploring its transformative impact, advantages, and the challenges it

GenAI-driven testing is a game-changer for software QA. It enables faster timelines, better use of scarce and specialized testing engineers, and much greater coverage and bug discovery. Accordingly, planning a GenAI-driven testing project is very different from planning a traditional testing project. This blog post explores six transformative aspects of AI-driven testing with an eye

Demand is higher than ever for rapid, reliable, and comprehensive software testing. That is driving the quest for innovative approaches that streamline testing processes while ensuring high-quality results. Enter GenAI-driven testing, a revolutionary advance that changes the game by allowing you to “Test More and Script Less.” With Appvance’s GenAI-driven platform AIQ in mind, this

Software QA is undergoing a sea change due to generative AI-driven testing. That begs the question of how to practice responsible AI in software testing. Hence, this post provides eleven considerations for responsible testing when using generative AI (GenAI). First, let’s note that responsible AI is an emerging area of AI governance covering ethics, morals

Generative AI has become a very hot topic over the past year, ever since ChatGPT exploded onto the scene. That general purpose tool and its mainstream competitors, e.g., Google Bard, are often thought to be the tools of choice for all uses of generative AI. However, that is not the case. Domain specific tools are

This is the fourth #BestPractices blog post of a series, by Kevin Parker. Introduction Testing is crucial to ensure the quality and reliability of applications. A strategic question that QA leaders must answer is what data to test against? One approach is to utilize production data for testing purposes. This seems convenient, but comes with a

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