My Testing Thoughts

  As GenAI continues to shape the future of software development, one powerful concept is making waves in the QA world: Model Context Protocol (MCP) . While it may sound technical at first, MCP is essentially the protocol that allows AI to maintain context and interact with external tools like JIRA, Slack, or Playwright during multi-step tasks. For testers, this means building intelligent assistants that can act like real QA team members. In this blog, we'll break down what MCP is, how it works, and how it can be used in software testing — with examples, advantages, and a few challenges to watch out for.   What is Model Context Protocol (MCP)? Think of MCP as the orchestrator behind intelligent conversations between a GenAI model and external systems. It's a structured way to maintain: Who said what (User vs AI) What AI is supposed to do (System instructions) Which tools can it use (Tool calls) What results were received (Tool responses)...

 Over the past year, Prompt Engineering has been the darling of the GenAI world. Tweak your prompt, get a better answer. Add more detail, get more context. Sounds great, right? If you’ve ever tried building AI agents that go beyond answering one-off questions — like automating test scenarios, helping with workflows, or making decisions across tools — you’ve probably hit a wall. And that’s where Context Engineering comes in. It’s not just the next trend — it's the foundation of truly intelligent AI agents . Prompt Engineering: Great for Questions, Not for Thinking Let’s start with what prompt engineering is good at. You give an LLM a prompt like: “Generate test cases for the login functionality with username, password, and forgot password link.” And it delivers. Job done — right? But what happens when your application has: Dynamic changes in functionality Reuse of components across modules Test dependencies that evolve over sprints Or user preferences like tes...

  Tired of AI giving too much or too little info? Here’s something that can help. Use this prompt at the start of your conversation: “If I add * at the end of my question, please provide a concise, to-the-point response. If I add **, provide a full and comprehensive response. If I do not provide any symbols, please provide a standard response.” Now, guide ChatGPT’s response style like this: 🔹 Add * → Short and crisp 🔹 Add ** → Deep and detailed 🔹 No symbol → Balanced by default ✅ Why it works: You stay in control of the depth No need to rewrite your prompt every time It works across any use case — writing, planning, learning, and ideating Small tweak. Huge flexibility. Try it and see the difference. 🚀  

When using GenAI tools like ChatGPT for test case generation, reviewing requirements, or analyzing user stories, we often need to provide context in chunks. 📌 Start your conversation with this prompt: “ I will be sending you several pieces of information in multiple messages. For each one, your only job is to acknowledge that you’ve received it with a simple message like “Acknowledged”—nothing more. Please do not take any action or provide any analysis or output until I send a final message with the instruction: “Now proceed.” Only then should you act on the information shared. “ 🛑 Why this works: -> It stops the model from responding after every input -> Ensures the model waits until you’ve shared all details -> Prevents premature or incomplete answers -> Mimics a real approach: gather context first, then act with precision It helps the model listen first, then act — just like a good teammate would. 💡 Whether you’re feeding in test data, requirement docs, or bug logs — ...

  As API testing becomes more central to modern software development, the tools we use to test, automate, and debug APIs can make a big difference. For years, Postman has been the go-to API client for developers and testers alike. But now, Bruno , a relatively new open-source API client, is making waves in the community. Let’s break down how Bruno compares to Postman and why you might consider switching or using both depending on your use case. ✨ What is Bruno? Bruno is an open-source, Git-friendly API client built for developers and testers who prefer simplicity, speed, and local-first development. It stores your API collections as plain text in your repo, making it easy to version, review, and collaborate on API definitions. 🌟 What is Postman? Postman is a full-fledged API platform that offers everything from API testing, documentation, and automation to mock servers and monitoring. It comes with a polished UI, robust integration, and support for collaborati...

  How to build smarter, more resilient automated tests? We’ve all been there — our Selenium test cases start failing because of minor UI changes like updated element IDs, renamed classes, or even reordered elements. It’s frustrating, time-consuming, and often the most dreaded part of maintaining automated tests. But what if your automation could heal itself? 💡 What is Self-Healing Automation? Self-healing automation  refers to the capability of a test automation framework to recover from minor UI changes by automatically trying alternative locators when the primary one fails. It’s like giving your test scripts a survival instinct. 🔨 🛠️ Implementation in Java + Selenium: Step by Step Step 1: Create a Self-Healing Wrapper We start by creating a custom class called SelfHealingDriver. This class wraps the standard WebDriver and handles locator failures gracefully. public   class   SelfHealingDriver { private   WebDriver driver ; public   SelfHealingDri...