Category: Blog

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

This is the fifth #BestPractices blog post of a series, by Kevin Parker. Excellent application performance and reliability is crucial in today’s software-dependent business environment. That’s why load testing — simulating realistic user loads to assess application performance — is a cornerstone of quality assurance. However, load testing can be resource-intensive, both in terms of time

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

We live in an API driven world. One upshot of this is that many applications must be tested at the API level, a reality not without complications. Fortunately, we at Appvance have engineered a great simplification to the challenges of API testing. It’s called the Appvance Services Workbench and comes as part of our Appvance

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

Forrester is out with a new Trends Report that speaks directly to what Appvance pioneered with AIQ, our generative AI powered software testing platform. Titled “The Future of TuringBots” and subtitled “TuringBots Will Be Development Teams’ Best Companions”, it provides helpful context about the rapid rise of generative AI across the entire Digital Value Stream

Testing and the CI/CD Process In the post, Continuous Testing: Required But Not Enough, our CTO Kevin Surace explored the necessity of Continuous Testing, but also its shortcomings. As he covered there, continuous testing requires a range of automated testing approaches, covering unit tests, API and integration tests, as well as more complete end-to-end tests.

This post explores the complexity of testing mobile apps and then provides guidance on how to master that complexity, including by using automation to reduce costs and cycle times while increasing coverage. The Complexity of Mobile App Testing  Mobile apps are increasingly complex, making them more difficult and expensive to test. What is causing this rapid

Application Coverage™ is the new gold standard metric of testing completeness, having supplanted the old-school metrics of Test Coverage and Code Coverage. This is because Application Coverage mimics user experience and can only be comprehensively achieved via generative-AI. Test Coverage and Code Coverage are limited because they are human dependent in terms of test conception,

This is the second #BestPractices blog post of a series, by Kevin Parker. Introduction Developers and quality assurance teams must collaborate to succeed in today’s fast-paced software development industry. By working together from the beginning, Dev and QA can incorporate best practices that make test automation easy and efficient, thereby accelerating release cycles and improving