Tag: software testing
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This is the fourth post in a four-part series from the article: Embracing AI First Software Quality Platforms: Transforming the Future of Software Testing Download the full eGuide here. Introduction Implementing an AI First testing platform requires a strategic approach that balances automation, human oversight, and continuous learning. By carefully dividing tasks between human engineers
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This is the third post in a four-part series from the article: Embracing AI First Software Quality Platforms: Transforming the Future of Software Testing Download the full eGuide here. Introduction The promise of AI in software testing is substantial, but realizing its full potential requires more than just implementing new technology. Organizations need to set
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This is the second post in a four-part series from the eGuide: Embracing AI First Software Quality Platforms: Transforming the Future of Software Testing Download the full eGuide here. Introduction The landscape of software quality assurance is undergoing a seismic shift with the advent of AI First Software Quality Platforms. Just as predictive maintenance has
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The need for robust testing methodologies in the world of software development has never been more critical. As applications become increasingly complex and interconnected, ensuring their reliability and functionality across diverse environments is a monumental challenge. However, with the advent of Gen AI, a transformative shift is underway in how software testing is approached, promising
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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
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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
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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
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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
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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
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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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