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Without a good test automation architecture in place, automation turns into a mess: brittle scripts, redundant tests, endless maintenance headaches. This guide breaks down when to build a proper architecture, why it matters more than you think, and how to design something that actually scales with you.

Your organization may be one of the many that is rushing to implement AI. But do you know where you fall on the AI maturity model? More than just a framework for understanding AI, the AI maturity model is a strategic guide that helps turn AI investments into tangible business results. A 2024 IDC study commissioned by Microsoft titled “The Business Opportunity of AI” found that organizations gain a $3.7x return for every $1 spent on generative AI.

AI code generators are revolutionizing the way software is developed. By leveraging the power of machine learning, these tools automate repetitive tasks, minimize human error, and accelerate the coding process. From GitHub Copilot to Keploy VS Code Extension, AI code generators are becoming essential for modern developers – whether they’re writing new features, debugging, or generating tests.

Large language models are under threat from a tactic called LLM grooming, where bad actors flood public data sources with biased or misleading content to influence AI training behind the scenes. Hemraj Bedassee , Senior Solutions Manager, AI Testing,

Agile and DevOps changed how we build software. Fast feedback, continuous delivery, and constant iteration are the new normal. But the faster you move, the more you risk breaking things—especially if your testing can’t keep up. That’s where test automation comes in. Not as a silver bullet, but as a strategic foundation for scaling quality at speed.

QA automation means turning manual test steps into code so they can run faster, more consistently, and at scale. Instead of executing the same test cases over and over, testers write scripts that do the work for them. These scripts validate core functionality, trigger workflows, and report pass/fail status automatically. We'll show you how to do it in this article, in-depth.

Collaboration across IT is increasingly critical to having a smooth and effective software release cadence. As software complexity grows, it becomes more difficult for individual team members to have a thorough understanding of all the different elements that comprise a successful software application.

Generative AI is quickly becoming ubiquitous in the software development space, with tools like Anthropic’s Claude offering rapid methodologies for code iteration, testing, and deployment. As new solutions, such as MCP (Model Context Protocol), are created to make integration more seamless, enterprises are adopting these AI solutions to optimize their development processes, a familiar challenge repeatedly arises: cost.

At Keploy, we wanted our tool to be part of the Github pipeline so that developers could be guaranteed that modifications made in pull requests could be safely merged and deployed. The only problem we had was ~ is it possible. The reason for this question is because Keploy uses eBPF to track network calls, which requires sudo capabilities. So the question becomes, will GitHub grant users sudo rights for a program they construct and execute on their systems?