Applying Automation-First Strategies to Software Testing Tools

In the last few posts we have explored how AI is reshaping the way software is designed, written, and deployed. But there is one final piece of the puzzle that often becomes the biggest bottleneck in the SDLC: The Verification/testing Loop. 📉 The traditional method of writing static test cases for every button and field is failing to keep pace with the sheer volume of code being produced. Here is how the testing landscape is evolving: 1️⃣ The Shift to Context-Aware Testing: Rather than engineers spending days drafting manual test scripts, AI models are now being used to analyze the original Business Requirement (BRD) and the code intent simultaneously. This allows the system to generate a testing framework that understands the "why" behind a feature, not just the "how." 🧠 2️⃣ Visual Perception : Anyone who has managed automation knows the pain of a test breaking just because a CSS class changed or a button moved by five pixels. Modern testing agents are being built to "see" the application like a human user. They validate the visual integrity and the user journey rather than just checking the underlying code structure. This moves the focus from "did the script find the element?" to "is the experience actually functional for the user?" 🚀 3️⃣ Risk-Based Resource Allocation: Not all code carries the same risk. AI is now being used to map "Hot Zones"-identifying areas of the codebase where complex logic or frequent changes historically lead to bugs. Instead of testing everything with the same intensity, teams are using these insights to focus their most rigorous verification where it actually matters. 🥊 🏗️ The Takeaway: Quality Assurance in 2026 is becoming less of a "phase" at the end of a sprint and more of a continuous, autonomous heartbeat. The focus is shifting from "Finding Bugs" to "Architecting Stability." How is your team handling the surge in code volume? #SoftwareTesting #QA #Automation #AI #SoftwareEngineering #VPspeak #TechLeadership #SDLC2026 #QualityEngineering

Something I wish I understood when I first started developing test automation is that it is more effective to think about it as a methodology to implemented than a product to be coded. I was initially focused on the tools and technologies that would enable us to build robust automated tests. However, with experience, I realized that the true value lies not just in the building but also in the application of these tests. It's crucial to understand where and how test automation is implemented in the development cycle. Integration into the CI/CD pipeline, aligning tests with business requirements, and ensuring that tests evolve with the product are all aspects that define the success of test automation. Moreover, test automation should be viewed as a continuous improvement process. It’s not just about setting it up once and forgetting about it; it involves regular updates, reviews, and adaptations to meet the changing needs of the software and the business. #TestAutomation #SoftwareDevelopment #ContinuousImprovement #DevOps

Automation ALONE won't give you the coverage you're looking for. It needs to be in line with manual testing ✅ Automation won’t yield instant results ✅ Automation usually comes with high upfront cost ✅ Your mindset is ready. What’s missing for successful adoption?  👉 A clear, step-by-step strategy. Here’s what I've seen working for our customers: 🎯 Define why you're thinking about automation, what the ideal end-state would be and, based on that, you'll be able to define the metrics that will help you measure your ROI (hint: end-state can't be to replace manual testing) 🔍 Evaluate your existing tests to determine which ones are good candidates for automation (hint: need to be run frequently, technically feasible, etc.) 🛠️ Choose tools that best match your team's skills and can scale across teams (hint: if your team can't write code, there are low-code/no code automation tools. If they want to learn how to code, these tools offer an easy on-ramp towards coded automation) 👥 Ensure your team has the necessary skills and training for test automation (hint: don't underestimate the need for proper education around test automation strategy. If you start it wrong, it's hard to scale later) 🌱 Start small and scale gradually (hint: this is key to capture the value/ROI in small steps from the beginning) 📈 Continuously monitor automation performance and refine your strategy (hint: if you're not getting ROI, something is wrong with your automation strategy. Always monitor your metrics) ⚖️ Leverage the strengths of both manual and automated testing for a comprehensive testing approach (hint: all automated testing enables is speed in test execution. Combining both your slower, but critically valuable, manual test executions with your super fast automated test executions will be key to achieving your desired coverage) By following these steps, I've seen our customers navigate the complexities of automation adoption and achieve a more efficient, reliable, and scalable testing process. 🚀 What other advice would you share? 🫵 #AutomationStrategy #SoftwareTesting #TestAutomation #QualityEngineering #SoftwareQuality Derek E. Weeks | Mike Verinder | Lucio Daza | Mush Honda | Gokul Sridharan | Hanh Tran (Hannah), MSc. | Daisy Hoang, M.S. | Parker Reguero | Florence Trang Le | Ritwik Wadhwa | Mihai Grigorescu | Srihari Manoharan | Phuong Nguyen

Let’s Talk Automation Testing — The Real, Practical Stuff We Deal With Every Day. If you’re in QA or an SDET role, you know automation isn’t about fancy frameworks or buzzwords. It’s about making testing faster, more reliable, and easier for everyone on the team. Here’s what actually matters: 1. Stability first A fast test that fails randomly helps no one. hope, you would agree? Teams trust automation only when it consistently tells the truth. Fix flakiness before writing anything new. 2. Manual + Automation = Real Quality Not everything needs automation. Manual testing is still crucial for user experience checks, exploratory testing, and edge cases that require human intuition. Automation supports manual testing — it doesn’t replace it. 3.Automate with intention Prioritize high-risk, high-usage flows. Login, checkout, search, payments — these are where automation creates real value. 4.Keep the framework clean and maintainable ( very imp step) Readable tests win. If someone new can’t understand or extend your suite, you don’t really have automation — you have tech debt. 5.Integrate early into CI/CD Automation only works when it’s continuous. Quick tests on every commit. 6. Make decisions based on data Look at failure patterns, execution time, and actual coverage. Data keeps automation aligned with the product, not just the backlog. At the end of the day, good automation suite is quiet, stable, and dependable — and it frees up manual testers to do the real thinking. 👉 What’s one practical testing tip you think every QA/SDET should follow? #AutomationTesting #SoftwareTesting #SDET #TestAutomation #QualityEngineering #ManualTesting Drop your thoughts — always great learning from others in the field. 💬🙂

My go-to framework when discussing and working on test automation strategy with clients and fellow practitioners is a simple one: The 5W1H method, also known as the Kipling method (yes, it was conceived by and named after the author of The Jungle Book). For example, I’ll ask: Why? (this is where it always starts) - Why are we spending time and effort on test automation? What do we want to achieve? What defines success for us? What? - What information are we looking for? What is important, and what can we defer or even ignore? This is often where the conversation around ‘are we going to automate existing regression tests?’ comes up. Where? - What interfaces are we covering? How can we make our test small and efficient? This is where we talk about testability. When? - How is writing, reviewing, running and maintaining tests part of the overall job of creating and releasing software? This covers making sure we invest the time, whether we use (A)TDD as well as which test goes where in a build and deployment pipeline. Who? - Which roles are involved? How do we make sure they have the right skills? How do we make sure the team actually works together as a team? How? - Which tools and techniques will we be using? What will be the coding standards for our test code? What do we build ourselves and what do we buy or reuse? Covering these six questions helps me enormously when I’m trying to paint a picture of what it takes to do test automation well. It’s also a powerful technique to get to the bottom of why things currently aren’t working out for a team or a company. Often, it turns out they’ve been focusing mainly on the ‘how’ (and then blame the tools) while paying lip service to the other questions, or even completely ignoring or skipping them. Yet, the ‘how’ by itself is almost never the problem, and changing the ‘how’ without also looking at the other questions is almost never the solution. This is the main reason I like this approach so much: it is simple, yet it allows for discussing a wide range of topics and questions that help teams improve their test automation efforts.

Traditional automated testing promises efficiency, but the reality is that tests crumble at the slightest UI change. It’s an all too common scenario: Spend weeks writing the perfect test, only for a minor button update to make half your test flash red. This ensues a cycle of constant firefighting that leaves QA teams exhausted and quality taking a hit. But what if tests could evolve as your product does? 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘀𝗵𝗶𝗻𝗲𝘀. At testRigor, we’ve helped companies like Netflix and Cisco reduce their reliance on implementation details and make their tests more stable and easier to maintain. We do this by marrying AI’s adaptability with human context. How? By allowing tests to be written in plain English. This approach doesn’t just make tests more stable — it captures nuances that often slip through the cracks of traditional automation. Product managers gain direct visibility into test cases, finally bridging the gap between vision and execution. Developers receive clear, actionable feedback, pinpointing issues accurately. QA team tackles complex edge cases and lets AI handle the grunt work. The result? A virtuous cycle of faster iterations, better products, and happier customers. Make your QA process an accelerator, not a bottleneck >> https://lnkd.in/eijgpWTj #AI #Automation #softwareengineering #softwareengineering