Automated Testing Frameworks

𝐓𝐡𝐞 𝐰𝐢𝐧𝐧𝐢𝐧𝐠 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐞𝐚𝐫𝐥𝐲-𝐬𝐭𝐚𝐠𝐞 𝐀𝐈 𝐬𝐭𝐚𝐫𝐭𝐮𝐩𝐬 — 𝐏𝐚𝐫𝐭 𝟓/𝟓 𝐄𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐄𝐯𝐚𝐥𝐬. This is the final part of the series — and the most important. In Parts 1–4, the discussion was on design partners, engineers watching users, AI-generated code, and daily shipping. All of that gives you speed. This part is about making sure that speed does not destroy your product quality. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐰𝐢𝐭𝐡 𝐬𝐩𝐞𝐞𝐝. This is where most early-stage AI teams fall short. They can build fast. They can ship fast. But they break things as fast as they fix them. A prompt change that improves one use case quietly degrades three others. A model upgrade slowly degrades quality in ways nobody catches until a customer complains. With AI product outputs being probabilistic, "correct" is often a judgement call. And the same input can produce different outputs on different days if you change your prompts, your model version, or your retrieval pipeline. 𝐍𝐨𝐧-𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐢𝐬𝐭𝐢𝐜 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦. The winning teams build measurement and observability into every layer — dev, CI/CD, staging, and production. The eval framework is the measurement system. Before you even generate your first-line of code, you have to get the test and eval framework setup. It could be deterministic checks, deterministic UI automations, LLMs as judge for generated AI quality checks, and a way to score the results across agents and systems. 𝐓𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐫𝐚𝐭𝐜𝐡𝐞𝐭. The hardest thing I have seen across portfolio companies, is building an end-to-end system for a way to move code in an autonomous way from dev to stage to production, without worrying that things may have broken. The most important thing that compounds is bringing test cases from production to stage to development in reverse. The more robust your eval infrastructure, the faster you can move. 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭 — 𝐚𝐥𝐥 𝐟𝐢𝐯𝐞 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. When all five are in place — real design partners, engineers next to users, AI-generated code with senior control, daily shipping, and eval-instrumented testing — the product quality compounds at a rate that is almost impossible for competitors to match. You are building a machine that converts user insight into shipped product in days, with quality that improves with every iteration. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: If you are an early-stage AI founder and you do not have all five in place, please fix it now. Start with design partners — everything else flows from there. The product quality that wins markets is not built in a lab. It is built in the field, with real users, at speed, with a safety net that gets stronger every day. Love to hear your experience in parts or full, if you are practicing any of the above.

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝗼𝗯𝘀𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗟𝗟𝗠𝘀. But most teams aren’t even testing the default behavior properly. A team we spoke to spent 6 weeks fine-tuning a model to reduce hallucinations in a customer support workflow. What they didn’t realize? The base model was already mostly fine. The hallucinations were triggered by edge-case phrasing—stuff their devs never thought to test for. What actually solved it? Not fine-tuning. Rigorous scenario-based testing with Ragmetrics. They fed real prompts, real tasks, real failure cases through our eval framework—and uncovered inconsistencies that only showed up under pressure. No more guessing. No more hallucinations at the worst time. Here’s the thing: 💡 You don’t need to fine-tune if you haven’t test-tuned first. Start with evaluation. Then optimize. If you’re building with LLMs and want to make sure your model actually behaves when it counts, happy to share what we’ve seen work. Just drop a comment or DM—I’ll send over the playbook.

Creating a Automation Framework for Netflix like application from scratch using PlayWright 1) Smart Architecture -> Fixtures-First Design (to introduce reusability) -> Custom auth fixtures with auto session management -> Browser context fixtures for parallel execution -> Data setup/teardown ensuring clean environments 2) Intelligent Data Strategy -> Static JSON for stable scenarios -> Faker.js for dynamic realistic data -> Live API data creation for fresh test conditions 3) Design Patterns That Work -> Factory Pattern: Dynamic test data creation -> Page Object Model: Encapsulated reusable components -> Strategy Pattern: Cross-browser testing -> Singleton: Optimized configuration management 4) Complete API Coverage -> REST APIs (CRUD, auth), GraphQL (queries, mutations), OAuth/JWT flows, Third-party integrations -> API + UI Combo: Live data creation through APIs feeding directly into UI test scenarios. 5) Advanced UI/UX Automation -> Visual regression testing -> WCAG accessibility validation -> Responsive design testing -> Performance metrics (Core Web Vitals) -> Cross-browser compatibility 6) ReUsability -> Components setup -> Custom assertions -> Config management through tsconfig, playwright.config, .env files -> Page Objects -> Data Factories 7) Testing Capabilities -> Cross-browser Testing: Chrome, Firefox, Safari, Edge support -> Mobile Testing: Device emulation and responsive design validation -> API Testing: REST and GraphQL endpoint validation -> Visual Testing: Screenshot comparison and visual regression -> Performance Testing: Load time and network performance metrics -> Accessibility Testing: WCAG compliance validation -> Tech Stack Playwright + TypeScript + Faker.js + Docker + CI/CD + PlayWright default Reports + Git Actions + APIs 🎪 Key Lessons -> Architecture investment scales exponentially -> OOP principles reduce complexity -> API-first approach ensures comprehensive coverage -> Mixed data strategy creates realistic scenarios -x-x- Learn PlayWright with JavaScript/TypeScript from Scratch with E2E Automation Course: #japneetsachdeva

Robot + Test Automation Tool + Microcontroller = Relief Hardware automation testing challenges had me in a chokehold for years. Until I discovered this approach: The problem: Manual hardware testing controlled my workflow. Cost me: Time, accuracy, and scalability Affected: Product development cycles and quality assurance Tried everything: Manual checks, partial automation, inconsistent results The breakthrough process: 1. Integrated microcontroller-based automated testing systems Immediate relief: Consistent and rapid evaluations with reduced human error. 2. Leveraged robotic automation for physical device testing Momentum built: Enhanced device verification through simulation of real-world conditions. 3. Combined software and hardware testing automation frameworks Freedom achieved: Streamlined processes with scalable, repeatable testing protocols. Result after implementation: → Improved accuracy and reliability in hardware validation → Reduced testing time and operational costs → Accelerated product development and higher quality standards The secret? Harnessing the synergy of microcontrollers and robotics to automate complex hardware testing tasks, enabling precise, efficient, and scalable verification. Your testing challenges have a solution. You just haven't implemented it yet. What hardware-related challenges does your automation testing process face? #TestAutomation #SoftwareTesting #QualityAssurance #TestMetry

Here’s my step-by-step action plan whenever I work with a client to help them get a new automation project started. Maybe it’s useful to you, too. 0. Write a single, meaningful, efficient test. I don’t care if it’s a unit test, an integration test, an E2E test or whatever, as long as it is reliable, quick and produces information that is valuable. 1. Run that test a few times locally so you can reasonably assume that the test is reliable and repeatable. 2. Bring the test under version control. 3. Add the test to an existing pipeline or build a pipeline specifically for the execution of the test. Have it run on every commit or PR, or (not preferred) every night, depending on your collaboration strategy. 4. Trigger the pipeline a few times to make sure your test runs as reliably on the build agent as it does locally. 5. Improve the test code if and where needed. Run the test locally AND through the pipeline after every change you make to get feedback on the impact of your code change. This feedback loop should still be VERY short, as we’re still working with a single test (or a very small group of tests, at the most). 6. Consider adding a linter for your test code. This is an optional step, but one I do recommend. At some point, you’ll probably want to enforce a common coding style anyway, and introducing a linter early on is way less painful. Consider being pretty strict. Warnings are nice and gentle, but easy to ignore. Errors, not so much. 7. Only after you’ve completed all the previous steps you can start adding more tests. All these new tests will now be linted, put under version control and be run locally and on a build agent, because you made that part of the process early on, thereby setting yourself up for success in the long term. 8. Make refactoring and optimizing your test code part of the process. Practices like (A)TDD have this step built in for a reason. 9. Once you’ve added a few more tests, start running them in parallel. Again, you want to start doing this early on, because it’s much harder to introduce parallelisation after you’ve already written hundreds of tests. 10 - ∞ Rinse and repeat. Forget about ‘building a test automation framework’. That ‘framework’ will emerge pretty much by itself as long as you stick to the process I outlined here and don’t skip the continuous refactoring.

🚀 Selenium Automation Project – Real-Time Use Cases Covered 🔍💻 I recently built a comprehensive Selenium Automation Framework to cover various real-world test scenarios. This project demonstrates key UI automation strategies and techniques required for web-based testing. 🧪 Use Cases Covered: ✅ Alerts ✅ Window and Tab Handling ✅ Broken Links Validation ✅ CSS Selectors & XPath Locators ✅ Checkboxes, Radio Buttons, and Drop-downs ✅ Date Picker Automation ✅ File Upload ✅ Frames & iFrames ✅ Conditional and Wait Methods (Explicit, Implicit, Fluent) ✅ JavaScript Executor ✅ Keyboard Actions & Mouse Hover ✅ Navigation Commands ✅ Headless Browser Testing ✅ Screenshot Capture ✅ Web Tables – Data Extraction & Validation ✅ Handling Browser Popups ✅ Utility Methods and Modular Structure ✅ First Test Setup (with JUnit/TestNG) 🔧 Framework Stack: Selenium WebDriver Java TestNG Maven Page Object Model (POM) JUnit GitHub for Version Control 📂 GitHub Repo: Explore the full project here 👉 https://lnkd.in/gA2stnEf #Selenium #AutomationTesting #Java #TestNG #SDET #QAEngineer #GitHub #UITesting #PageObjectModel #LinkedInLearning #CareerGrowth #TestAutomation

The AI tool that wins your demo isn't always the right one. A real evaluation starts with the right question. Start here. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝟭: Does this tool solve a problem we've already quantified? If no, stop. You're evaluating based on excitement, not need. Document the baseline: time spent, error rate, and cost per output. Without it, you can't measure success. If yes, move to Question 2. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝟮: Can we test it on real inputs from real workflows? If no, that's a red flag. Vendor demos use cherry-picked inputs. So do internal "trials." Pull 50 real inputs and require the tool to handle them before purchase. If yes, move to Question 3. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝟯: Does the output quality hold up at the worst 20% of inputs? This is where most tools quietly fail. AI tools are easy to evaluate on clean inputs. The real test is messy, edge-case data. Pull your 10 hardest inputs: - The most ambiguous customer email - The most unstructured document - The most complex query your team handles - The longest realistic input If it breaks here, it'll break in production. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝟰:  What's the total cost, not just the licensing fee? License cost is often less than half the total. The full cost includes: - Implementation and onboarding - Supervision and review (discount time-savings claims by 30–50%) - Rework for incorrect outputs - Change management and training - System integration If the vendor can't model this, you don't have a real ROI. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝟱: Will it integrate with our existing systems? A great standalone tool is often worse than a decent integrated one. Check for: - Native CRM, email, and storage integrations - API access if needed - SSO and admin controls for 10+ users - Data export options If integration costs more than the tool, the math rarely works. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝟲: Can we run a structured 30-day pilot with documented success criteria? If they won't agree, walk away. Real pilots have: - A documented problem and baseline - Real users running real workflows - Success metrics for quality, reliability, cost, and adoption - A one-page post-pilot decision before signing This separates the 5% of AI buyers who see real returns from the 95% who don't. If all six answers are yes, the tool is probably worth buying. If any answer is no, close that gap before signing. What this framework pays off for: - Avoiding multi-year contracts on tools that fail in month two - Building ROI your CFO can audit - Separating vendor marketing from production reality - Reducing AI tool sprawl before it starts The pressure to buy fast is the biggest reason most AI pilots fail. Vendors push for deals before pilots finish. Internal teams push for tools before baselines are set. Slow down evaluation. Speed up rollout once the tool clears every question. Subscribe to The AI Report for daily AI news → https://lnkd.in/eVxtGmfV

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.

Here's the LLM evaluation stack I recommend to every team: Layer 1: Unit Tests (DeepEval) Stop treating AI as a mystery box. Integrate with Pytest to run assertions on every build. → Test individual components (retrievers, generators, tools) → Run in CI/CD to block regressions → Move from vibe-checking to deterministic engineering Layer 2: Metric Suite (50+ SOTA Metrics) Quantify performance with academic-grade metrics, not just "looks good" scores: → Hallucination: Is it making things up? → Faithfulness: Is it strictly grounded in your context? → Agentic Trajectory: Did it pick the right tool and use the correct arguments? → G-Eval: Define custom, subjective criteria in plain English. Layer 3: Synthetic Data Evolution Don't wait for user logs to find your bugs. → Generate thousands of "Golden" test cases from your docs in minutes → Automatically cover complex edge cases → Scale your testing without a single manual label Layer 4: Continuous Monitoring Evaluation doesn't stop at deployment. → Track performance drift in real-time → Get a "Rationale" (the why) for every production failure → A/B test prompt versions with statistical confidence DeepEval handles all 4 layers in one framework. One framework: ✓ 50+ research-backed metrics ✓ Pytest-native syntax ✓ Synthetic data generation ✓ Full Agent & RAG support This is how you ship AI with actual confidence. (100% Open-Source) GitHub Repo - https://lnkd.in/gQ3zCcZN Don't forget to ⭐️

Your AI Pilot proved the technology. It did not prove the operating model. Most executives can recite the AI adoption numbers. Far fewer can explain why experimentation keeps rising while enterprise-scale deployment remains stuck. McKinsey’s latest global survey found that 88% of organizations now use AI in at least one business function. Yet 62% remain in experimentation or piloting, while only 7% describe their AI programs as fully scaled. A proof of concept asks: Can the technology work under controlled conditions? Production asks: Can the organization make it work repeatedly, safely, and economically? Those are not the same test. Here is what changes between the demo and the deployment. 1/ The dirty-data test → Pilot: A curated dataset cleaned by the same team that built the system → Production: Missing fields, stale records, conflicting systems of record, inconsistent formats, and undocumented exceptions 2/ The volume test → Pilot: Fifty controlled prompts run by the project team → Production: Production-shaped traffic at real volume, with unpredictable inputs and users who do not follow the instructions 3/ The ownership test → Pilot: Sponsored by a steering committee and managed by a temporary project team → Production: Owned by one business leader who controls the workflow, accepts the operational risk, and is accountable for the outcome 4/ The integration test → Pilot: A sandbox disconnected from the systems and workflows it is expected to improve → Production: Integrated with legacy infrastructure, identity systems, databases, approval processes, security controls, and downstream applications 5/ The adoption test → Pilot: Used by the enthusiasts who volunteered → Production: Used by employees with different incentives, skill levels, workloads, and willingness to change 6/ The operating-control test → Pilot: A one-time evaluation before the executive readout → Production: Continuous evaluation, drift monitoring, incident ownership, escalation thresholds, rollback procedures, and a support runbook 7/ The value test → Pilot: Judged by how impressive the demonstration looked → Production: Judged by whether it improves revenue, cost, risk, cycle time, quality, or another measurable business outcome Organizations do not scale AI by moving the same pilot into a larger environment. They scale it by building the operating model around it: → Reliable data → Production-grade infrastructure → Named accountability → Redesigned workflows → User adoption → Continuous oversight → Measurable business value Before the next pilot readout lands on your desk, ask: If our AI pilot cannot survive contact with your worst data, your busiest week, and your most skeptical employee, what exactly did it prove? Need help with your AI strategy? Book a strategy call: https://lnkd.in/gEY5pN7z Save for future reference.