Tag: AI-driven testing

In the ever-evolving landscape of software development, the need for robust and efficient testing methodologies has become paramount. Traditional testing practices often fall short when it comes to handling the complexities of modern applications. Enter AI-driven non-functional testing, a game-changer that leverages artificial intelligence to simulate load under various conditions and detect security vulnerabilities in

In the ever-evolving landscape of software development, ensuring the reliability and functionality of applications is paramount. Traditional testing methods are valuable, but the dynamic nature of modern software demands a more adaptive and comprehensive approach. This is where the synergy of human-guided exploration and AI-driven testing comes into play, providing a powerful solution for enhancing

In the new era of GenAI-driven testing, software development managers find themselves in a challenging yet promising landscape. The power of artificial intelligence has significantly enhanced our ability to unearth bugs, but it comes with a unique challenge: Vastly more errors are being uncovered than ever before. In this cheat sheet, we’ll explore the art

Most of our posts focus on the QA team, e.g., the Impact of AI on Test Teams from September. However, this one explores the impact of GenAI-driven testing on the development team. This is because GenAI has totally changed how QA teams pursue their mission, but has also significantly changed how dev teams interface with

AI-driven testing changes everything for testing teams. These Best Practices ensure best outcomes.  I’ve recently published a series of posts on Best Practices for different aspects of software QA in the age of AI-driven testing. This post serves as a portal to them. Before listing the posts, it’s worth noting that everything has changed in

AI-driven testing leads to new forms of team composition and compensation. AI is a force-multiplier for test teams, a reality that’s driving new thinking about how test teams are composed and compensated. This is because AI-driven testing enables test teams to finally keep pace with dev teams, albeit with a radically reformed approach to the

This is the third #BestPractices blog post of a series, by Kevin Parker. Introduction The emergence of artificial intelligence (AI) has revolutionized software quality and test automation, including by transforming the way we approach test design and execution, and in offering new possibilities and challenges. The Appvance IQ (AIQ) generative-AI testing platform embodies these transformations, possibilities

The benefits of AI-driven testing go well beyond automatic test-script generation, profound and game-changing as that is. However, auto test-script generation is robustly covered elsewhere on this blog, so this post introduces two downstream benefits of AI-driven testing: Intelligent Test Prioritization and Test Results Analysis. AI-aided test prioritization and results analysis are each transformative in

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