Scott’s Testing Blog

 This article was written with the assistance of AI. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into software products is rapidly changing the landscape of software development and, consequently, software testing. Quality Assurance (QA) professionals are now encountering systems where AI models play a crucial role in various functionalities. To navigate this evolving environment effectively, QA teams need to understand the underlying technologies that power these AI integrations. One such significant innovation is the Model Context Protocol (MCP) . This article aims to demystify the Model Context Protocol (MCP) specifically for QA teams. We will explore what MCP is, why it's becoming increasingly important in the realm of AI-driven software, how it conceptually works, and what implications it holds for software testing. By understanding MCP, QA professionals can better prepare for the challenges and opportunities presented by AI-powered applications...

 This article was written with the assistance of AI. The landscape of software testing is undergoing a significant transformation with the advent of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) . These powerful AI models, such as ChatGPT, Gemini, and Claude, possess the remarkable ability to generate human-like text, understand natural language, and even assist with code generation. As a result, software testers are increasingly looking to leverage these capabilities to enhance their productivity, improve test coverage, and streamline various testing activities. However, the effectiveness of these AI tools heavily relies on the quality of the input they receive. This is where the crucial skill of prompt engineering comes into play. Prompt engineering is the art and science of crafting effective prompts – the instructions or questions provided to an AI model – to elicit the desired and accurate responses. For software testers, mastering prompt eng...

This article was written with the assistance of AI.  The landscape of software development is undergoing a profound shift, driven by the rapid advancements in artificial intelligence. One of the most intriguing and potentially transformative trends emerging from this AI revolution is vibe coding . Initially a seemingly whimsical term, vibe coding has quickly gained traction as a legitimate and increasingly powerful approach to software creation, where developers describe their intentions in natural language, and AI models generate the corresponding code. This paradigm shift, championed by leading AI researchers and embraced by developers seeking increased productivity, is particularly well-suited for the realm of automated testing , offering an unprecedented opportunity to streamline the creation and maintenance of both API and UI tests. What Exactly is Vibe Coding? The term "vibe coding" gained significant recognition following a viral post by Andrej Karpathy, a prominent...

This article was written with the assistance of AI. The landscape of software testing is undergoing a seismic shift, driven by the rapid advancements in Artificial Intelligence (AI). Among the most talked-about and potentially transformative branches of AI is   Generative AI (GenAI) . While the term might conjure images of futuristic robots writing flawless code or autonomously uncovering every hidden bug, the reality, at least for now, is more nuanced and offers a powerful set of tools for software testing professionals. However, the buzz surrounding GenAI can also lead to confusion and uncertainty. This article aims to demystify generative AI for software testers, exploring what it is, how it works, its current and potential applications in testing, and the crucial role testers will continue to play in this evolving landscape. What Exactly is Generative AI? At its core,  generative AI refers to a class of artificial intelligence models designed to generate new, original cont...