Why Automated Testing Matters for Software Maintainers

The AI Coding Revolution Is Here, But Are We Testing for It? As AI-assisted development reshapes how we build software, I've been thinking a lot about something that is talked about often but doesn't always get the focus it deserves: automated testing. At JPMorganChase, we're embracing AI coding tools to accelerate delivery, reduce toil, and empower our teams to focus on the work that matters, reducing cognitive load of repetitive tasks. But speed without safety is just risk in disguise. Here's what I believe every leader (and this is broader than technology) needs to consider right now: • AI writes code faster than humans can review it manually. If your testing strategy is still largely manual, you're already behind. AI-generated code can introduce subtle logic errors, security vulnerabilities, or edge-case failures that look perfectly reasonable on the surface. Automated testing is no longer a best practice, it's a non-negotiable safeguard. • Test coverage is your new quality contract. When AI is your co-developer, the test suite becomes the specification. If you can't describe expected behavior in a test, you can't trust what the AI builds. Investing in robust unit, integration, and regression testing frameworks is investing in the integrity of your entire delivery pipeline. • Shift-left testing amplifies AI's value. It doesn't slow it down. Some worry that rigorous testing will negate the speed gains from AI coding. The opposite is true. When automated tests are embedded early in the development lifecycle, AI tools can iterate faster, self-correct, and validate outputs in real time. Testing enables velocity; it doesn't constrain it. • Your teams need to evolve alongside the tools. The best teams of tomorrow won't just write code. They'll architect test strategies, evaluate AI outputs critically, and build systems that are observable and verifiable by design. We owe it to our teams to invest in this skill evolution now. At the scale we operate, serving millions of customers, the cost of a defect isn't just technical. It's trust. And trust, once broken, is hard to rebuild. AI is a force multiplier. But multiplying without a strong foundation multiplies risk just as fast as it multiplies output. Build fast. Test smarter. Ship with confidence. I'd love to hear how other leaders are thinking about quality engineering in the age of AI. What's working for your teams? #AIEngineering #SoftwareTesting

After 25+ years in #QA, one architectural pattern keeps repeating. Most escaped defects were not caused by people being unable to test well. They were caused by the system’s inability to be re-tested frequently enough. Modern software changes constantly. Daily commits. Daily merges. Daily deployments. Humans can test deeply. But a system without automation cannot revalidate behavior every day, across environments, at scale. That is where defects escape. Automation exists for one core architectural reason: repeatability at speed. High-quality automated coverage enables a system to: 1) Re-run the same critical paths daily or continuously 2) Revalidate regression after every meaningful change 3) Preserve confidence that yesterday’s behavior still works today 4) Allocate human effort to exploration, risk analysis, and design feedback - not repetition When automation is missing, the system is forced into trade-offs: 1) Test less often 2) Test smaller slices 3) Rely on memory, heroics, and hope Hope is not a strategy. The goal of automation is not to replace humans. It is to make frequent, repeatable validation a built-in property of the system. Without that property, quality cannot keep up with change. That lesson eventually appears in every large system. #QualityEngineering #TestAutomation #QA #SoftwareTesting #QASolver

The quest for quality often leads software organizations down a paradoxical path: adding more manual checks and approvals that actually make quality worse, not better. Release processes that require manual QA signoff, security review, or executive approval might feel safer, but they create long, unpredictable feedback cycles that hide problems and increase risk. Consider what happens when teams batch up multiple changes for a big release requiring manual review. Engineers context-switch to new tasks while waiting for approval. When issues are found, the original context is lost and debugging becomes complex. Meanwhile, more code is being built on potentially problematic foundations. The cycle repeats, creating a growing backlog of changes waiting for review. This pattern appears in many forms. Manual QA phases that take days or weeks. Change review boards that meet monthly. Pre-release checklists that grow ever longer. While each addition to the process aims to improve quality, the cumulative effect is often the opposite: larger, riskier deployments that are harder to troubleshoot when things go wrong. The most effective teams recognize that rapid, automated feedback is far more valuable than manual process gates. They invest in automated testing, continuous integration, and tooling that catches issues early. They deploy small changes frequently rather than batching them up. When manual reviews are needed, they happen continuously rather than becoming bottlenecks. So, remember: - Large batches of changes increase risk, not safety - Manual approvals create queues that hide problems - Long feedback cycles make debugging more difficult - Automated checks scale better than manual processes - Frequent small deployments tend to be lower risk than infrequent large ones The path to better quality is enabling faster feedback through automation and smaller batch sizes—it's not adding more manual processes. Truly embracing quality at scale requires letting go of the illusion of control that manual processes provide.

In a recent conversation with a telecom engineering team, they shared that their biggest pain point was not deploying new features. It was testing changes in a legacy billing system written in a mix of #COBOL and #ABAP. Every small update required weeks of regression testing because no one fully trusted the test coverage. Critical scenarios lived in production behavior, not in test suites. Teams relied on tribal knowledge and manual validation to avoid outages that could impact millions of customers. This challenge shows up across industries where systems have grown for decades under constant regulatory and commercial pressure. Automated testing struggles when the intent of the code is unclear. One learning we have seen at Adapts is that #AI can help teams reconstruct test intent from existing code paths and data flows. #Modernization becomes safer when testing is grounded in understanding, not guesswork. #LegacyCode #SoftwareMaintenance #SoftwareArchitecture #Code2Wiki #CodeKnowledge #SDLC

I see this dilemma more and more across teams today during my conversations. Many decision-makers feel confident because automated regression tests run after every deployment. On paper, everything looks under control. In reality, companies experience defect leakage, client complaints, and ongoing frustration with functional, integration and usability issues. But here is my question: How do we know the automation is actually covering what truly matters? Automation only checks what we told it to check. It does NOT question assumptions, notice new patterns, or understand how real users behave. If the original scenarios were incomplete, outdated, or based on the wrong understanding of the product, the tests will still pass and provide a false sense of safety. That’s where the danger lies. Your 100% passing rate on automated regression testing: edge cases, negative scenarios, integrations, data issues, usability problems, or real-world workflows that were never turned into scripts in the first place. Real confidence does NOT come from having automation. It comes from knowing: a. Why each test exists b. Which risks are covered and which ones we are accepting after evaluation c. What assumptions the automation is built on d. Who is continuously questioning and evolving that coverage Automation is incredibly powerful, but it needs human thinking behind it. Without that, it does NOT build confidence, it just makes us feel comfortable and it is a HUGE Risk by itself. And that comfort is NOT the same as quality. HIST Human Intelligence Software Testing guides teams toward intelligent automation, not automation done simply to look impressive without a clear purpose. Thoughts?

Five years ago, we started MuukTest with a question: Could we make "Great Software Testing and Automation" easier? My cofounder Renan Ugalde & I each have 20+ years of Software Development & QA experience. We've seen Bad Software Testing and Great Software Testing - way too much of the former, not enough of the latter: Bad Software Testing:  - Nonexistent professional testing team, expecting "developers to do all their own testing" (which 'works' until it doesn't) - Wild, non-disciplined, exploratory testing that doesn't help  - Testing that's treated as a second-class citizen in an engineering org, not as a partner - Reactive, under-resourced testing teams  - Testing with deficient coverage and no automation at all - Testing that's treated as the last step in an assembly line - Massive teams of testers, treated like a boiler room - Testing that slows engineering down I spent years of my life in Software QA and Testing roles like this… where I spent my Christmas holidays frantically, manually running regression tests across an entire application that *had* to be released yesterday. This is bad Software Testing. It is stressful for everyone and doesn't lead to good outcomes. Great Software Testing is:  - Proactive - A mix of (smart, disciplined, strategic) manual exploration and automation - Partner to engineering - Happens throughout the engineering cycle - Performed and coordinated by small teams of testing experts  - Employs amazing tools - Helps engineering move faster - Delivers insights, not more work Since 5 years ago, we have always believed that 'Great, Fast, Efficient Software Testing' will be made possible by AI. Bad testing happens because of bad ideas and bad tools, but with AI helping with a lot of the heavy lifting of test automation and maintenance, 'Great, Fast, Efficient Software Testing' is possible for more teams. Proud of our work in AI, making Great Software Testing possible for ANY software team. Great Software Testing means better software, faster development, happier customers, and better outcomes for all. 

Test automation involves using specialized tools and scripts to automatically execute tests on software applications. The primary goal is to increase the efficiency and effectiveness of the testing process, reduce manual effort, and improve the accuracy of test results. ⭕ Benefits: ✅ Speed: Automated tests can run much faster than manual tests, especially when running large test suites or repeated tests across different environments. ✅Reusability: Once created, automated test scripts can be reused across multiple test cycles and projects, saving time in the long run. ✅Coverage: Automation can help achieve broader test coverage by executing more test cases in less time. It can also test various configurations and environments that might be impractical to test manually. ✅Consistency: Automated tests execute the same steps precisely each time, reducing the risk of human error and improving the reliability of the tests. ✅Regression Testing: Automated tests are particularly useful for regression testing, where previously tested functionality is checked to ensure it still works after changes are made. ⭕Challenges: ✅Initial Setup: Creating and maintaining automated tests requires a significant initial investment in terms of time and resources. ✅Maintenance: Automated tests need to be updated as the application changes. This can lead to additional maintenance overhead, especially if the application evolves frequently. ✅Complexity: Developing and managing automated tests can be complex, particularly for applications with dynamic or changing interfaces. ✅False Positives/Negatives: Automated tests might produce false positives or negatives if not carefully designed, leading to misleading results. ⭕Common Tools: ✅Selenium: A widely used tool for web application testing that supports various programming languages. ✅JUnit/TestNG: Frameworks for Java applications that provide annotations and assertions for unit testing. ✅Cypress: A modern testing framework for end-to-end testing of web applications. ✅Appium: An open-source tool for automating mobile applications on various platforms. ✅Jenkins: Often used in continuous integration/continuous deployment (CI/CD) pipelines to automate the execution of test suites. ⭕Best Practices: ✅Start Small: Begin with a few test cases to build your automation framework and gradually expand as you refine your approach. ✅Maintainability: Write clean, modular test scripts that are easy to maintain and update. ✅Data-Driven Testing: Use data-driven approaches to test various input scenarios and ensure comprehensive coverage. ✅Integrate with CI/CD: Incorporate test automation into your CI/CD pipeline to ensure automated tests run with each code change. Review and Refactor: Regularly review and refactor your test scripts to improve their efficiency and reliability. In summary, test automation can significantly enhance the testing process, but it requires thoughtful implementation and ongoing maintenance to be effective.

𝐐𝐀 𝐚𝐧𝐝 𝐃𝐞𝐯𝐎𝐩𝐬 𝐟𝐨𝐥𝐤𝐬: still trying to keep up with CI/CD while relying on manual testing? 😕 That assumption can derail your entire pipeline before you even reach production… CI sounds straightforward: developers merge frequently, automated tests run, and clean code flows to production. But here’s the reality… Each code change triggers layers of testing — unit, integration, UI, performance, and security. Each release cycle gets shorter. And expectations for stability keep going up. Manual testing can’t keep up, especially when builds are pushed several times a day and feedback needs to happen in minutes. And this isn’t just about speed, it’s about risk. If your test coverage is thin or brittle, you don’t just slow things down, you ship bugs faster. Modern CI/CD pipelines depend on automated testing — not as a nice-to-have but as a foundation. You need tests that are stable across environments, resilient to UI changes, and ready to run in parallel across devices the moment code is committed. There are platforms now that plug directly into your CI/CD flow, capable of running tests in parallel, across environments, with minimal maintenance and no manual bottlenecks. If your QA process isn’t fully aligned with your CI/CD velocity, that friction’s going to show, and fast. How are you adapting your testing to meet the speed of modern delivery? 🚀 #CICD #QATesting #DevOps