Tag: AI

There’s a dangerous myth circulating in the QA industry: that any AI is good AI. Tool vendors are racing to slap on “AI” features—copilots, agents, test case creators—all in an effort to look modern. But beneath the flashy UI and prompt-driven wizardry is a hard truth: these tools are actually slowing down experienced QA professionals. And there’s now

For years, test automation has promised to accelerate software delivery and improve quality. Yet many teams still struggle with brittle scripts, time-consuming maintenance, and incomplete test coverage. As applications grow more complex and release cycles speed up, traditional automation often can’t keep pace. Enter AI-first testing—a smarter approach that uses artificial intelligence to write, run,

The landscape of work is undergoing a rapid transformation, with AI at the forefront of this change. One of the areas most impacted by AI is Quality Assurance (QA), a crucial component in software development. AI testing is set to revolutionize the QA landscape, bringing significant implications for the job market and the skills required

As technology advances at an exponential rate, the role of artificial intelligence (AI) in software quality assurance (SQA) has become increasingly prominent. From automating mundane tasks to enhancing overall efficiency and productivity, AI has proven itself as a powerful tool in the arsenal of QA teams. But what does the future hold? Can AI go

[SANTA CLARA, CA, December 19, 2023] — Appvance, the leader in generative AI for software quality, is excited to announce the launch of Appvance IQ (AIQ) 5.0, a groundbreaking update that marks a significant leap forward in the world of AI-native automated testing.  This release features the game-changing Generative AI V3, a comprehensive update that enhances

Ask yourself “what would I do differently if my test team were one thousand people strong and all as good as my very best automation engineer?” That is the team you are about to lead. Software testing has entered a new era with the arrival of GenAI. But, GenAI’s manifold benefits only come after properly

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-enabled software testing changes the game for testing teams and their leaders. Here are four best practices and an important tip for making the most of this unprecedentedly powerful automation technology. Best Practice #1: Segment test cases for human or AI creation. Identify the critical test cases that humans should write. Have test engineers write

Introduction Software test automation has been a cornerstone of software quality for decades. However, the traditional approach to test automation and maintenance has been plagued by high costs, limited resources, and the need to prioritize critical test cases. In recent years, Artificial Intelligence (AI) and especially generative AI has emerged as a game-changer in the

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