When evaluating AI for testing, prioritize approaches that keep teams in control and maintain end-to-end testing connectivity ...

Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

In today's market, businesses need to move quickly, deliver seamless digital experiences and build trust with users. In the face of these demands, software testing has evolved from a necessary process ...

New SmartFox AI capability scores tests by proven impact, ending guesswork of test prioritization. QA teams don't lack ...

When LambdaTest was founded, the problem it set out to solve was far more contained but with the rise of AI-generated code ...

Test automation has been pivotal in accelerating software releases, but it came with a high learning curve that limited its reach. No-code testing platforms helped ease that by enabling teams to ...

Choosing the right test management tool directly impacts your team's ability to ship quality software fast. QA teams today juggle manual tests, automated suites, scattered documentation, and ...

As enterprises rethink their testing strategies, many teams are reviewing AI test automation tools that can help modernize QA ...

LambdaTest, a cloud-based software testing platform, has raised $38 million in a Series D funding round as it looks to expand its presence in existing markets and improve its AI capabilities. The ...

Fuzz testing, commonly known as fuzzing, is an automated software testing strategy that involves providing invalid, unexpected, or random data as inputs to computer programmes. Its primary aim is to ...