Test Case Generation Using Codex Agents

Agentic Testing: a New Chapter in Software Quality The last weeks has shown something fascinating: the same AI coding agents we use every day in our IDEs are quietly becoming testing agents. Not theoretical research toys but practical, reasoning-driven workers that can analyse codebases, explore APIs, drive UIs with MCP tools, and even generate and evolve entire test suites. I’ve written a deep dive exploring this: https://lnkd.in/dQY4b7bz In the article I cover: - How coding agents already behave like general-purpose engineering agents - White-box agentic testing: reading your code, analysing coverage, simulating execution - Black-box agentic testing: exploratory API and UI testing via terminal, Playwright MCP and Chrome DevTools MCP - Real examples: Spring Boot backend + React frontend - The benefits, limitations and the cost problem - How agentic testing fits (and doesn’t try to replace) the classic test pyramid If you’re curious how AI can transform the daily work of testers, SDETs and developers Would love to hear your experiences and whether you see these agents becoming part of your testing workflow.

🤖 What if AI agents could test themselves? New research shows how multiple AI agents working together can generate and verify their own test cases - potentially solving a major bottleneck in AI development. A paper from Splunk introduces MAG-V, a multi-agent framework that tackles two critical challenges in AI development: How do you test AI assistants without endless customer queries? MAG-V uses three specialized AI agents working together: an "investigator" generates test questions, an "assistant" answers them, and a "reverse engineer" creates alternative questions to verify the answers. Think of it like a study group where one student creates practice problems, another solves them, and a third validates the solutions from different angles. How do you verify if an AI's problem-solving approach is correct? Here's where it gets fascinating for data scientists: Instead of using expensive GPT-4 evaluations, MAG-V uses familiar ML techniques! They extract six key features (like Levenshtein distance and semantic similarity) from the AI's solution paths and train simple models like k-NN and Random Forest. The result? Their k-NN model matched GPT-4's accuracy (82%) while being more reliable and cost-effective. The team found that using just one feature - Edit Distance - was enough to achieve 80% accuracy. Sometimes simpler really is better! 🎯 The most surprising finding? Adding a simple system prompt ("You are a helpful AI Assistant") improved GPT-4's evaluation accuracy by 12% - a powerful reminder that even small prompt engineering choices can have major impacts. Paper link in comments. #AIResearch #MachineLearning #LLMs #ArtificialIntelligence #DataScience

🚀 From Manual QA Thinking → Agentic AI Automation As an SDET, one challenge always stood out: 👉 Writing test cases manually 👉 Converting them into automation takes time 👉 Maintaining scripts becomes repetitive So I built something different — an Agentic AI Playwright Framework 🤖 💡 What it does: Instead of doing everything manually, I created two AI Agents working together: 🔹 Agent 1: QA Test Case Creator Takes a URL (e.g., Amazon) → Analyzes UI like a QA → Generates: ✅ Positive cases ❌ Negative cases ⚡ Edge cases ♿ Accessibility tests Exports everything into Excel 🔹 Agent 2: Automation Script Generator Reads the Excel → Filters automation‑ready test cases → Auto‑generates: Playwright scripts Page Object Model (POM) Project Flow Diagram 📊 Project Flow Overview 🌐 Input URL → 🤖 Agent 1 (Test Case Generator) → 📄 Excel Output 🤖 Agent 2 (Script Generator) → 🧪 Playwright Specs → 🚀 Test Execution ⚙️ Tech Stack: Playwright + TypeScript | Page Object Model (POM) | Multi‑Agent AI 🔥 Why this matters: ✔ Reduces manual effort drastically ✔ Bridges QA → Automation gap ✔ Scales test coverage faster ✔ Keeps framework maintainable #QA #SDET #AutomationTesting #Playwright #AI #AgenticAI #SoftwareTesting #TestAutomation #Innovation #AITesting

𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝘀 𝗱𝗼𝗻’𝘁 𝘀𝗰𝗮𝗹𝗲. I keep seeing this take. Let me show you what’s actually happening. When you ask an LLM to “generate 20 test cases” with zero context, yes — you’ll get garbage. No auth handling. No test data strategy. No framework awareness. But that’s not an AI problem. That’s a skill problem. This is how Playwright’s new test agents work → https://lnkd.in/dF2-NDtF I customized these agents for the new playwright-cli — the token-efficient CLI that replaces the heavier MCP approach. CLI commands = smaller context = faster, cheaper AI operations. 🚀 Ready-to-use repo: 👉 https://lnkd.in/dbaY6j6x 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀 👇 ① 𝗕𝘂𝗶𝗹𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝗙𝗶𝗿𝘀𝘁 Define your business flows, auth patterns, and framework conventions as reusable skills. This isn’t about specific test patterns — the skills are fully customizable, so every team encodes their own definition of good tests. ② 𝗣𝗹𝗮𝗻 → 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 → 𝗛𝗲𝗮𝗹 🎭 Agents explore the app, create a structured test plan, generate tests that match your conventions, and auto-heal when the UI changes. Drop it into your .claude/ directory. Customize the skills for your app. Watch it generate tests that actually scale.

Anyone can build an Agent today. But only few can do this: Traditional testing relies on fixed inputs and exact outputs. But agents speak in language, and there’s no single “correct” response. That’s why we test Agents using other Agents by simulating Users and Judges. I built such a pipeline to test Agents with the help of other Agents by using Scenario. Here's my 100% open-source tech stack: - CrewAI for Agent orchestration. - LangWatch Scenario to build the eval pipeline. - PyTest as the test runner. The underlying process is explained in the animation of the video below: Step 1) Define three Agents: - The Agent you want to test. - A User Simulator Agent to act like a real user. - A Judge Agent for evaluation. Step 2) Let your Agent and User Simulator Agent interact with each other. Step 3) Evaluate the exchange using the Judge Agent based on the specified criteria. The LangWatch Scenario framework orchestrates this process. It is a library-agnostic Agent testing framework based on simulations. Key features: - Test Agent behavior by simulating users in different scenarios and edge cases. - Evaluate at any point of the conversation using powerful multi-turn control. - Integrate any Agent by implementing just one call() method. - Combine with any LLM eval framework or custom evals. Find a link to the GitHub repo in the comments! ____ Find me → Avi Chawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄❓  🤖 The latest Playwright release introduces AI-powered Playwright Agents that can plan, generate, and even heal your tests! In a recent Playwright Live session, Debbie O'Brien and Ben F. unpacked this groundbreaking feature. Debbie showcased a live demo of how the new 𝗣𝗹𝗮𝗻𝗻𝗲𝗿, 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿, 𝗮𝗻𝗱 𝗛𝗲𝗮𝗹𝗲𝗿 𝗮𝗴𝗲𝗻𝘁𝘀 work together to create a comprehensive test suite for a web application, clarifying their power and potential. This is a huge leap forward for test automation! (Link to video in comments) After you've had a chance to watch, 𝗜'𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝘆𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀: 💎 What's your initial reaction to AI-powered test automation? 💎 How do you see Playwright Agents impacting your current testing processes? 💎 What are the potential benefits and challenges of integrating this into your workflow? Let's discuss in the comments! 👇 Playwright Agents Overview: 1️⃣ The Planner Agent: 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Explores your application to create a comprehensive test plan blueprint. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: Uses a seed.spec.ts file for context, navigates UI via interactive exploration, and analyzes DOM/accessibility to identify testable features. 𝗢𝘂𝘁𝗽𝘂𝘁: A detailed markdown (.md) file outlining test cases and user stories. 2️⃣ The Generator Agent: 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Transforms the Planner's blueprint into functional Playwright test code. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: Focuses on specific plan sections, verifies steps by performing them in a browser before writing code (the most powerful feature!), and then generates code directly from a successful test log. 𝗢𝘂𝘁𝗽𝘂𝘁: A new .spec.ts file with high-confidence, verified test code. 3️⃣ The Healer Agent: 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Automatically diagnoses and fixes failing tests due to UI changes. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: Reproduces failures, debugs in real-time, compares expected vs. actual states, and corrects the test code. It can also flag potential application bugs. 𝗢𝘂𝘁𝗽𝘂𝘁: An updated test file with corrected code, saving significant debugging effort. #RexJonesII #Playwright #TestAutomation #AI #SoftwareTesting

🤯 this paper is absolutely mind-blowing!! and just in from last week.. 𝗧𝗵𝗲 𝗕𝗶𝗴 𝗣𝗶𝗰𝘁𝘂𝗿𝗲:  Instead of building specialized AI agents for each coding task (testing, debugging, repair) 𝘁𝗵𝗲𝘆 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝗨𝗦𝗘𝗮𝗴𝗲𝗻𝘁 - 𝗮 𝘂𝗻𝗶𝗳𝗶𝗲𝗱 "𝗔𝗜 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿" that can orchestrate multiple capabilities dynamically. think of it as evolving from having separate tools to having one intelligent craftsperson. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗕𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝗰𝗲: the Meta-Agent architecture - it breaks down rigid workflows into composable "actions" (CodeRetrieval, EditCode, ExecuteTests, etc.) and uses ReAct-style reasoning to decide which action to invoke next. No more fixed pipelines! 𝗧𝗵𝗲 𝗠𝗲𝗺𝗼𝗿𝘆 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻:  they introduce a structured "task state" as consensus memory between actions - storing relevant code locations, test results, and patches. this prevents information loss as the agent switches between different types of work. 𝗨𝗦𝗘𝗯𝗲𝗻𝗰𝗵 𝗶𝘀 𝗚𝗼𝗹𝗱:  they created a meta-benchmark combining SWE-bench-verified, REPOCOD, SWT-bench, plus REPOTEST (a new derivative they created) into one unified interface. this lets them test everything from bug fixes to test generation to feature development in one framework. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿:  - 33.3% success rate across all tasks vs 26.8% for OpenHands CodeActAgent. - it matches specialized AutoCodeRover (45.6% vs 46.2% on SWE-bench-verified) while being applicable to WAY more task types. 𝗧𝗵𝗲 𝗦𝗲𝗹𝗳-𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝗴𝗶𝗰: the agent actually adapts its workflow based on task type: - starts with reproduction for bug fixes, but begins with TestRetrieval for regression testing. then converges to alternating EditCode and ExecuteTests. no manual configuration needed! 𝗪𝗵𝗮𝘁'𝘀 𝗠𝗶𝘀𝘀𝗶𝗻𝗴: they're honest about limitations - edge case handling, - backtracking from dead ends, and - patch overfitting (though they reduced this to 10.5% vs previous 31% rates!) But they outline clear paths forward. this feels like the first real step toward AI that can be a genuine team member in software development, not just a specialized tool. 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗰𝗼𝗱𝗶𝗻𝗴 𝗷𝘂𝘀𝘁 𝗴𝗼𝘁 𝗺𝗼𝗿𝗲 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴! 🚀 link in the comments below 👇