Broaden Your Approach to Software Testing

🚀 The Future of Software Testing: What We Need to Learn Today 🚀 As someone who's been in software testing for 25 years, I've seen how much has changed — from manual testing to automation, from Selenium to Playwright, and now, the next big thing: AI-powered testing. And if you're like me, you're probably thinking: what's next? Looking ahead to the next 5 years, here's what I believe testers should focus on: 👉 Shift-Left & CI/CD Testing Gone are the days when testing happens only at the end of the process. The future is all about testing early, continuously, and with CI/CD pipelines. It’s time for testers to dig deeper into automating at every stage, from unit tests to APIs. Testers should focus on: Integrating testing into CI/CD pipelines, collaborating closely with dev teams, and automating tests earlier in the cycle to get faster feedback. 👉 AI-Powered Test Automation AI is no longer just a buzzword—it’s already here, and it’s transforming how we design and run tests. From generating test cases automatically to fixing broken scripts, AI is going to make our lives easier. We’ll still need to validate and guide AI-generated results, but the tools are getting smarter. Testers should focus on: Exploring AI tools that assist in test generation, learning how to prompt these tools effectively, and validating AI-generated outputs to ensure quality. 👉 Security Testing Security is going to be at the core of everything we do. With more apps moving to the cloud and being built on microservices, knowing how to perform security tests (DevSecOps) will be crucial. Testers should focus on: Learning security testing techniques, familiarizing themselves with vulnerabilities, and integrating security checks into the testing process from day one. 👉 Data-Driven Testing In the future, data is king. Not just in terms of coverage and defect trends, but also using real-time data to understand system performance. Testers should focus on: Using tools that provide insights into system performance (metrics, logs, traces) and incorporating them into the testing process for more effective decision-making. 👉 Domain Knowledge & System Complexity Software is getting more complex. Whether it’s working in fintech, healthcare, or any other specialised field, understanding the domain and system complexity is key. The more testers know about how the system works, the better their testing becomes. Testers should focus on: Deepening their knowledge of specific domains (e.g., finance, healthcare) and understanding architectural complexities like microservices to better navigate testing challenges. 👉 Leadership & Soft Skills With automation doing a lot of the heavy lifting, the future of testing will need testers to take on leadership roles—designing strategies, mentoring teams, and driving quality discussions across the board. #SoftwareTesting #Automation #AI #ShiftLeft #DevOps #SecurityTesting #QualityEngineering #LearningJourney

Don’t Focus Too Much On Writing More Tests Too Soon 📌 Prioritize Quality over Quantity - Make sure the tests you have (and this can even be just a single test) are useful, well-written and trustworthy. Make them part of your build pipeline. Make sure you know who needs to act when the test(s) should fail. Make sure you know who should write the next test. 📌 Test Coverage Analysis: Regularly assess the coverage of your tests to ensure they adequately exercise all parts of the codebase. Tools like code coverage analysis can help identify areas where additional testing is needed. 📌 Code Reviews for Tests: Just like code changes, tests should undergo thorough code reviews to ensure their quality and effectiveness. This helps catch any issues or oversights in the testing logic before they are integrated into the codebase. 📌 Parameterized and Data-Driven Tests: Incorporate parameterized and data-driven testing techniques to increase the versatility and comprehensiveness of your tests. This allows you to test a wider range of scenarios with minimal additional effort. 📌 Test Stability Monitoring: Monitor the stability of your tests over time to detect any flakiness or reliability issues. Continuous monitoring can help identify and address any recurring problems, ensuring the ongoing trustworthiness of your test suite. 📌 Test Environment Isolation: Ensure that tests are run in isolated environments to minimize interference from external factors. This helps maintain consistency and reliability in test results, regardless of changes in the development or deployment environment. 📌 Test Result Reporting: Implement robust reporting mechanisms for test results, including detailed logs and notifications. This enables quick identification and resolution of any failures, improving the responsiveness and reliability of the testing process. 📌 Regression Testing: Integrate regression testing into your workflow to detect unintended side effects of code changes. Automated regression tests help ensure that existing functionality remains intact as the codebase evolves, enhancing overall trust in the system. 📌 Periodic Review and Refinement: Regularly review and refine your testing strategy based on feedback and lessons learned from previous testing cycles. This iterative approach helps continually improve the effectiveness and trustworthiness of your testing process.

One of the common practices followed in software testing is to allocate 25-30% of the development effort towards testing. However, this method can at times mislead us, particularly when we face seemingly minor changes that unfold into complex challenges. Take, for instance, an experience I had with a retail client aiming to extend their store number format from 4 to 8 digits to support business expansion. This seemingly straightforward task demanded exhaustive testing across multiple systems, significantly amplifying the testing workload beyond the initial development effort—by a factor of 500 in this instance. 💡 The Right Approach 💡 1️⃣ Conduct a thorough impact analysis: Understand the full scope of the proposed changes, including the affected components and their interactions. 2️⃣ Leverage historical data: Use insights from past projects that are similar in nature to make informed testing estimates. 3️⃣ Involve testing experts early on: The sooner they are in the loop, the better they can provide realistic perspectives on possible challenges and testing needs. 4️⃣ Adopt a flexible testing estimation model: Move away from the rigid percentage model to a dynamic one that takes into account the specific complexities of each change. Has anyone else experienced a similar situation? How do you navigate the complexities of testing estimations in your projects? Your insights are appreciated! #softwaretesting #qualityassurance #estimation

🚀 Maximizing Success in Software Testing: Bridging the Gap Between ITC and UAT 🚀 It's a familiar scenario for many of us in the software development realm: after rigorous Integration Testing and Certification (ITC) processes, significant issues rear their heads during User Acceptance Testing (UAT). This can be frustrating, time-consuming, and costly for both development teams and end-users alike. So, what's the remedy? How can we streamline our processes to ensure a smoother transition from ITC to UAT, minimizing surprises and maximizing efficiency? Here are a few strategies to consider: 1️⃣ *Enhanced Communication Channels*: Foster open lines of communication between development teams, testers, and end-users throughout the entire development lifecycle. This ensures that expectations are aligned, potential issues are identified early, and feedback is incorporated promptly. 2️⃣ *Comprehensive Test Coverage*: Expand the scope of ITC to encompass a broader range of scenarios, edge cases, and real-world usage patterns. By simulating diverse user interactions and environments during testing, we can uncover potential issues before they impact end-users. 3️⃣ *Iterative Testing Approach*: Implement an iterative testing approach that integrates feedback from UAT into subsequent ITC cycles. This iterative feedback loop enables us to address issues incrementally, refining the product with each iteration and reducing the likelihood of major surprises during UAT. 4️⃣ *Automation Where Possible*: Leverage automation tools and frameworks to streamline repetitive testing tasks, accelerate test execution, and improve overall test coverage. Automation frees up valuable time for testers to focus on more complex scenarios and exploratory testing, enhancing the effectiveness of both ITC and UAT. 5️⃣ *Continuous Learning and Improvement*: Cultivate a culture of continuous learning and improvement within your development team. Encourage knowledge sharing, post-mortem analyses, and ongoing skills development to identify root causes of issues and prevent recurrence in future projects. By adopting these strategies, we can bridge the gap between ITC and UAT, mitigating risks, enhancing quality, and ultimately delivering superior software products that meet the needs and expectations of end-users. Let's embrace these principles to drive success in our software testing endeavors! #SoftwareTesting #QualityAssurance #UAT #ITC #ContinuousImprovement What are your thoughts on this topic? I'd love to hear your insights and experiences!

"We have to run a full regression test suite on every build!" First: you don’t *have* to do anything. There is no law of nature, nor any human regulation, that says you must repeat any particular test. You *choose* to do things. In the Rapid Software Testing namespace, we say that regression testing is *any testing motivated by change to a previously tested product*. When a product changes, risk is not evenly distributed. A brief pause for some analysis of where the changes might affect things can help focus your testing on plausible risk. Test — that is, challenge — the idea that a change had only the desired effects in the area it was made, and didn’t introduce undesirable effects. Test things that might be connected to or influenced by that change. It might make sense to do *some* testing in places where you believe risk is low — to reveal hidden risks. If you want to find problems that matter, though, diversifying your techniques, tools, and tactics is essential. Rote repetition can limit you badly. Obsession with looking for problems in exactly the same way as you've already looked displaces two things: 1) Your ability to find problems that were there all along, but that your testing has missed all along; and 2) Your ability to find new problems introduced by a change that your existing set of tests won’t cover. Don’t fixate on tests you’ve done before. Consider the gap between what you thought you knew about the system before the change, and what you need to know about the product as it is now. It’s the latter that’s the most important bit, and your old tests might not be up to the task. Need help convincing management of this? Let me know.

After hiring and mentoring hundreds of testing professionals, I've noticed one factor that consistently separates those who thrive from those who stagnate: the ability to evolve beyond tool expertise. The silent career killer for testers isn't lack of technical skill—it's becoming the "Selenium expert" or the "performance testing tool specialist" rather than the problem solver who happens to use those tools. Career-limiting patterns I've observed: 👉 Defining yourself by the tools you use rather than problems you solve 👉 Focusing on test execution rather than test strategy 👉 Measuring success by activity metrics rather than business impact 👉 Staying in your technical comfort zone rather than learning the business domain The most successful testing professionals in 2025 aren't tool experts—they're business problem solvers who leverage tools to deliver value. What helped you move beyond tool expertise in your testing career? #careeradvice #testingprofession #professionalgrowth #qualityengineering #softwaretesting #careerpath #skilldevelopment #softwaretestingcompany #softwaretestingservices #awesometesting #vtest VTEST

Too many people, especially in large enterprise projects, continue to cling to the outdated notion that coding and testing are distinct disciplines. This is not just archaic; it's a glaring oversight of what drives real innovation and quality. Just as citizen developers have shattered the conventional barriers by crafting solutions with the tools at their disposal, so too must professional software engineers embrace the full spectrum of development responsibilities—testing included. The differentiation between a coder and a software engineer isn't in the complexity of the problems they solve, but in their approach to creating solutions. True professionals don't just write code; they foresee potential pitfalls, architect solutions resilient to real-world challenges, and validate their functionality through rigorous testing. This isn't merely a preference; it's a fundamental aspect of engineering excellence. Consider the practical benefits when engineers test their own creations: enhanced understanding of the code, faster bug identification and resolution, and, most importantly, a product that's built to last. It's not about burdening developers with additional tasks; it's about empowering them to deliver great work, unfettered by traditional role limitations. Integrating testing into the development workflow isn't a revolutionary concept—it's a return to the roots of problem-solving and innovation. Methods like Test-Driven Development (TDD) and Continuous Integration (CI) aren't just strategies; they're testaments to a mindset that values foresight, accountability, and craftsmanship. So, let's set aside the dated distinctions and embrace a holistic view of software engineering—one where coding and testing are inseparable elements of the craft. By fostering a culture that champions this comprehensive skill set, we not only elevate the quality of our projects but also the standard of professionalism in our field.

Testing in small chunks fits in easily in a schedule, is more easily shared, is easier to explain. It adapts well to fast moving cycles, changes in priorities. It delivers what people need to know closer to when they need to know it. I find more and more as I try to help someone improve their engineering approach, when I look at the testing, I suggest they stop thinking about the testing activity as a large phase, as a "test pass" and instead think of it as a set of topically focused explorations - chartered sessions - delivered in small chunks. So many improvements are impeded by treating the testing as a giant box of stuff while so many improvements can build upon testing delivered as a series of shorter, easy to understand activities. This approach harmonizes well with shortened development iteration cycles. As developers change their habits from taking weeks to introduce a change to delivering that change as a series of smaller changes delivered a day or so, sometimes hours apart, the testing has to adapt in a way that still gives the developer, and the team, the information they need to manage development and delivery of the overall story. The big box of testing stuff doesn't fit anymore. Small, chartered testing sessions fit well. A session may fit in as part of the work before submitting code. It may address capabilities awakened from multiple changes deployed over the span of a few days that lead up to a larger, riskier integration change coming next. Packing testing into smaller boxes makes it easier to give a box to different people on the team. Rather than stacking the whole team up waiting for the test team to finish the pass (that rarely anybody ever gives them enough time to finish), hand tasks out and spread the effort around. You learn quickly that not all testing tasks are the same, demand the same sort of talent, time, and resources. Some tasks are much easier for the developer to handle, and some for a tester. The big box of testing stuff makes it difficult to make that distinction. Smaller boxes make it easier. #softwaretesting #softwaredevelopment You can find more of my articles and cartoons about testing in my book Drawn to Testing, available in Kindle and paperback format. https://lnkd.in/gB4NS4BS

In an ideal world, we’d get instant feedback on software quality the moment a line of code is written (by AI or humans) (we’re working hard to build that world, but in the meantime); how do we BALANCE speed to market with the right level of testing? Here are 6 tips to approach it: 1 - Assess your risk tolerance: Risk and user patience are variable. A fintech app handling transactions can’t afford the same level of defects as a social app with high engagement and few alternatives. Align your testing strategy with the actual cost of failure. 2 - Define your “critical path”: Not all features are created equal. Identify the workflows that impact revenue, security, or retention the most; these deserve the highest testing rigor. 3 - Automate what matters: Automated tests provide confidence without slowing you down. Prioritize unit and integration tests for core functionality and use end-to-end tests strategically. 4 - Leverage environment tiers: Move fast in lower environments but enforce stability in staging and production. 5 - Shift Left: Catching defects earlier saves time and cost. Embed testing at the pull request, commit, and review stages to reduce late-stage surprises. 6 - Timebox your testing: Not every feature needs exhaustive QA. Set clear limits based on risk, business impact, and development speed to avoid getting stuck in endless validation cycles. The goal is to move FAST WITHOUT shipping avoidable FIRES. Prioritization, intelligent automation, and risk-based decision-making will help you release with confidence (until we reach a future where testing is instant and invisible). Any other tips?

Testing AI Software ≠ Testing Traditional Software 🧪🤖 Let’s use a simple analogy. Testing traditional software is like grading a math test in school. 🧮 You ask: What is 12 x 8? The answer is either 96 or it’s wrong. ✅❌ Everything is predictable, rules-based, and objective. Now imagine testing AI software. That’s more like grading an essay in an English class. 📝 You ask: “Describe the impact of climate change on coastal cities.” One student writes about rising sea levels and flooding. Another focuses on economic disruption and relocation. A third writes a poetic reflection on environmental loss. Are any of them wrong? Not really. But are they all equally relevant, clear, and impactful? That’s where it gets tricky. This is the reality of testing AI. You're no longer asking, “Did the software produce the correct answer?” You're asking: “Is this useful?” “Is this safe?” “Is this fair, accurate, and aligned with user expectations?” Here’s how to approach testing an AI system: 🧠 Define what “good” looks like — with expert rubrics, not hardcoded rules. 👨⚕️ Involve humans in the loop — especially when context and nuance matter. 📊 Use hybrid metrics — BLEU/ROUGE + domain-specific checklists. 🔁 Test behavior, not just output — especially across edge cases and updates. Traditional testing checks answers. AI testing checks judgment. And if we treat one like the other, we risk missing what really matters. Curious how you’re adapting your QA for the AI age? #AI #SoftwareTesting #ProductDevelopment #QualityAssurance #AIinHealthcare #Mindbowser #HumanInTheLoop #AIEvaluation