Secure MCP servers against prompt injection, data leaks, and denial-of-wallet with four practical, OWASP-aligned gates from code to production. Runnable code.
Learn about why QA-as-a-phase persists, the costs it creates, and how teams can transition to continuous quality across the software development lifecycle.
Learn how to choose the right API testing framework, including REST Assured, Supertest, pytest, Postman, Karate, and Keploy, for better API test automation.
Learn to build a strong testing pipeline to ensure code quality, as in enterprises today most code is generated by AI agents at a huge volume and scale.
CI optimization using Git diff and JGit to selectively run impacted Karate tests, reducing regression execution while preserving safe fallback coverage.
Five harness portability tests — memory, tools, skills, orchestration, and governance — that reveal what you trade when committing to an AI agent platform.
Learn why 100% code coverage doesn't guarantee software quality, and how behavior coverage and mutation testing provide more meaningful testing insights.
In this blog post, we will see how to perform a performance test on a retrieval-augmented generation (RAG) application properly, covering both speed and correctness.
Azure Databricks and Microsoft Fabric overlap, but they're built for different priorities. Databricks for data engineering, ML, open-source, and Spark workloads.
When browser automation fails, the thrown error is usually three steps removed from the actual cause. Treat the Playwright trace as the source of truth.
A practical guide to feature engineering at scale with Azure Databricks, covering distributed data processing with Spark and reliable storage with Delta Lake.
AI-generated code introduces integration failures that spec-based tests cannot catch. Regression testing grounded in real production behavior is the fix.