Generative AI automation targets coding, debugging, documentation, and testing workflows in SDLC processes SAN JOSE, ...

Testing places unique demands on AI. Errors carry real business risk, and fragile tests or slow updates can quickly erode trust in results. As a result, while momentum around AI in testing is strong, ...

Most testing strategies collapse under the weight of modern software development demands. But speed requirements continue to increase while application complexity grows, which creates an impossible ...

From generating test cases and transforming test data to accelerating planning and improving developer communication, AI is having a profound impact on software testing. The integration of artificial ...

AI-driven automation is becoming increasingly integrated into the world of software development: documentation generation, coding assistants, automated testing, and deployment orchestration, among ...

The demand for data engineering solutions is growing significantly. According to a Market Data Forecast report, the global big data and data engineering market was valued at $75 billion in 2024 and is ...

Combine AI-generated tests with intelligent test selection to manage large regression suites and speed up feedback ...

Locator failures often lead to broken and flaky automation scripts, resulting in frequent test breaks, increased maintenance costs, and delays in development cycles. With Smart Heal, LambdaTest ...

Testing APIs and applications was challenging in the early devops days. As teams sought to advance their CI/CD pipelines and support continuous deployment, test automation platforms gained popularity, ...

Adopting AI isn’t a PR exercise, and it isn’t a case of “the more the better.” The key to successful AI integration is less about how much you have—after all, too much automation can be frustrating ...

Artificial intelligence is no longer a futuristic abstraction—it’s an economic force reshaping how work gets done, how wealth is created, ...