AI is rapidly becoming embedded in our software, but how do we test it effectively? My latest article, 'Beyond Black Boxes: Practical Strategies for QA Teams Testing Embedded AI,' dives into the unique challenges of AI testing – bias, drift, data dependency – and offers practical solutions for QA Managers, SDETs, and testing teams. Let's move beyond the 'black box' and ensure the reliability and fairness of AI-powered products.

This article emphasizes the importance of code reviews and coding standards for maintaining a healthy and productive software testing framework. It explains how AI can automate and enhance these processes, improving code quality and reducing technical debt. The article advocates for a proactive approach to prevent issues like duplicated code and inconsistent practices, ensuring long-term efficiency and reliability. 

The article shares a six-pillar philosophy for effective Software Quality Assurance (QA) mentorship, emphasizing Empowerment, Accountability, Courage, and Humility (EACH). It aims to guide the next generation of QA professionals, particularly SDETs, by providing practical strategies and insights. The author developed this philosophy in response to a question about their mentoring approach, highlighting core values in leadership and professional development. 

Even the most thorough software testing cannot compensate for fundamental flaws in the software development process. Bad development practices introduce defects early, overwhelm testing efforts, and ultimately hinder the delivery of high-quality software. To truly enhance software quality, organizations must focus on improving the entire development lifecycle, fostering collaboration, and integrating testing throughout. 

Technical debt in QA, like flaky tests and brittle automation, acts as a hidden tax that cripples development velocity and increases risk. Addressing this requires strategic investment in test health and fostering true shared ownership between Dev and QA teams.