Using Software Testing for Problem Identification

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.

QA Scenario: A strong QA process ensures the software works not just when things go right, but also when things go wrong. Here are key scenario types every QA should include in their test coverage: 1️⃣ Positive Scenarios (Happy Path) ✅ Verifying the application works as expected under normal, valid conditions. Example: User logs in with correct username & password. 2️⃣ Negative Scenarios 🚫 Testing with invalid inputs or actions to ensure the system handles errors gracefully. Example: Entering wrong password multiple times triggers account lock. 3️⃣ Edge & Boundary Scenarios 📏 Testing limits and extreme cases in input ranges, data size, or conditions. Example: Uploading a file exactly at the maximum allowed size. 4️⃣ Integration Scenarios 🔗 Ensuring modules and third-party services work together without issues. Example: Payment gateway correctly processes an order and updates inventory. 5️⃣ Real-World Scenarios 🌍 Simulating how actual users interact with the system in day-to-day situations. Example: User starts filling a form, loses internet, then resumes after reconnecting. 6️⃣ Non-Functional Scenarios ⚡ Testing performance, security, usability, and compatibility. Example: Application load time stays under 2 seconds for 10,000 concurrent users. 💡 Key Insight: A well-rounded QA approach doesn’t just ensure functionality — it prepares the system for the messy, unpredictable real world. “Bugs hide where no one looks — so test beyond the obvious.” #SoftwareTesting #QAScenarios #QualityAssurance #TestCoverage #BugPrevention

When you test something, and doing the same steps the same way reproduces different results, what do you do? We notice this more when we get this behavior via automated scripts, but that is because automation makes it easier to repeat the same thing many times. Think about that. You might not be seeing inconsistent behavior when directly interacting with the product, "flake" as some call it, ONLY because you are moving more slowly. Repeat what you are doing ten, a hundred, a thousand times, and you might see a difference. This is one of the benefits of automation, rapid repetition. Inconsistent behavior is one of the most difficult category of application defect to catch and identify. Virtually none of the bugs that get out to customers were something that were easy to reproduce in a consistent way. And yet, when people see inconsistent results from their automated suite, they call it "flake" and throw it away. They call it "fixing," but a lot of "fixing" is about carefully steering the script away from whatever it is that is behaving differently. Maybe you should take the opposite approach. Embrace the flake. Do you have a script delivering different results? Amplify it. Figure out whatever it is doing and do more of it, do it more aggressively. There is something interesting in that flake, and you probably need to know what it is. Cartoon inspired by a real problem that I came across this week. I had web page throwing access denied every few times I ran the script, and I thought it might have been something I was doing wrong. Instead of tossing it out, I looped it. I found that I was getting that access denied failure about 20% of the time the script ran. I check my code in that way, attach the trace to a bug report and submit. #softwaretesting #softwaredevelopment #embracetheflake

Three critical bugs in applying discounts and where they should have been caught A customer applies a discount, expects a lower total, and proceeds to checkout. If that process fails, it directly impacts revenue and trust. Here are three critical bugs related to applying discounts and the tests that should have caught them. 1. Incorrect discount calculation – A 20% discount on a $100 cart incorrectly subtracts $80 instead of $20 due to a formula error. A unit test should have verified the calculation logic. 2. Multiple discount codes don’t apply correctly – A user applies “SUMMER10” (10% off) and “WELCOME5” ($5 off), but only the second one is applied. A service-layer integration test should have checked how discount codes interact and whether stacking rules are enforced correctly. 3. Discount disappears at checkout – A customer applies “BLACKFRIDAY50” (50% off), sees it reflected in their cart, but at checkout, the discount is gone. An end-to-end test should have ensured the discount persists throughout the entire purchase flow. Each of these failures highlights a gap in testing. Unit tests validate logic, integration tests confirm service interactions, and end-to-end tests ensure the feature works in real-world use. Catching these issues early prevents costly mistakes in production. The best organizations empower testers to evaluate the unit and integration tests instead of manually testing basic functionality. If those tests are well-designed and cover edge cases, testers should never have to manually verify discount application. Otherwise, they are stuck setting up multiple types of discounts, stacking scenarios, and checkout flows—work that should already be covered by automation. This allows testers to focus on exploratory testing, edge cases, and true user experience issues, rather than verifying what a test suite should already guarantee. #softwaretesting #quality

The day I started 🔥 treating testing like problem-solving—not just test execution—my mindset completely changed! Earlier, I thought testing was about executing steps and logging defects. But when I started asking why, how, and what if—I began uncovering real product issues and adding more value to the team. 😎 Here’s how I shifted from tester to problem-solver: 1. Asked ‘Why’ Before Writing Test Cases → “Why is this feature built?” helped me test real user flows, not just acceptance criteria. 2. Focused on Impact, Not Just Steps → Logged defects with impact: “This breaks user onboarding, not just a button click.” 3. Explored Beyond the UI → Validated DB changes, API responses, backend logic—not just visible bugs 4. Communicated Early & Often → Asked questions during grooming, clarified use cases, raised red flags early 5. Suggested Improvements, Not Just Bugs → “Can we make the error message more helpful?” → “Can this flow be optimized for fewer steps?” 💡 Pro Tip: Being a tester isn’t just about finding what’s wrong—it’s about making the product better. What’s one habit that helped YOU think beyond basic test cases? Need help leveling up your mindset as a QA? https://lnkd.in/dUYHp9Af #SoftwareTesting #QAMindset #ProblemSolving #SDET #QualityEngineering #CareerGrowth #Topmate

💡Meta's research introduces 𝗔𝗖𝗛 (𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗖𝗵𝗲𝗰𝗸 𝗳𝗼𝗿 𝗛𝗮𝗿𝗱𝗲𝗻𝗶𝗻𝗴), a new 𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 using LLMs for generating more 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝘂𝗻𝗶𝘁 𝘁𝗲𝘀𝘁𝘀. ACH uses 𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 to generate 𝘁𝗮𝗿𝗴𝗲𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝘀 that can detect specific issues, like privacy vulnerabilities, and ensures they are buildable, reliable, and meaningful. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴? • 𝗠𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 helps identify gaps in test coverage by introducing small changes (mutants) to the code, which are then checked by the test cases. • 𝗟𝗟𝗠𝘀 are used to 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝘁𝗲𝘀𝘁𝘀, making the process faster and more efficient, with a focus on issues like privacy and security. • The method results in 𝗯𝗲𝘁𝘁𝗲𝗿 𝗰𝗼𝘃𝗲𝗿𝗮𝗴𝗲, ensuring that tests are actually catching bugs and improving code quality before release. As someone building in this space, this research is a great reminder of 𝗵𝗼𝘄 𝗔𝗜 𝗰𝗮𝗻 𝗺𝗮𝗸𝗲 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘀𝗺𝗮𝗿𝘁𝗲𝗿—𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗳𝗮𝘀𝘁𝗲𝗿. We're on it to make it generally available with Keploy 🐰 🔜.🔥 The idea of hardening code against potential vulnerabilities through automated, AI-driven tests sounds promising, let's take testing beyond traditional approaches. 🚀 Check out the full paper: “Mutation-Guided LLM-based Test Generation at Meta” https://lnkd.in/gUWgbvgB #AI #MutationTesting #LLM #SoftwareTesting #Security #Keploy

🚀 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!

Just last week, a friend who leads Engineering at a fintech company told me something that stuck with me: "Our team spent 30+ hours debugging a memory leak in production that was introduced by a PR merged 3 weeks ago. The engineer who wrote it had already moved on to different tasks, and context-switching back to that code was incredibly painful." This is the hidden tax of detecting non-functional issues too late in the development cycle. Studies show bugs cost 10-100x more to fix when found in production vs. development. What if you could shift ALL your non-functional testing left? Not just unit tests, but performance, load, memory, and security tests BEFORE merging PRs? We've been obsessed with solving this problem at Signadot. Our approach: create lightweight "shadow deployments" of services being changed in PRs, without duplicating entire environments. The results we're seeing are game-changing: - Memory leaks caught before they wake up on-call engineers at 3AM - 30% performance degradations identified during code review, not in production - Load tests running automatically on PRs, preventing capacity issues I'm curious: what's the most painful non-functional issue your team discovered too late? And what would change about your development process if you could catch these issues at PR time? #ShiftLeft #SoftwareEngineering #DevOps #PerformanceTesting

I wrote the perfect test case. Then, the bug hit production. And I said it... “Oops, I missed that bug.” But that moment made me ask a better question: What if we designed our systems to make mistakes more challenging to make in the first place? Enter: A brilliant (and wildly underrated) concept from Toyota’s production line— Poka-Yoke. 👀 What’s that? It means “mistake-proofing.” Not fixing bugs. Not catching them late. But stopping them before they ever happen. This blew my mind. And it’s not just for factories. It’s powerful for software, too. Here’s how Poka-Yoke shows up in testing: 🧩 Form Field Validations → Stop lousy input before it enters the system. ⚙️ Environment Pre-checks → Is the test environment right? The test doesn’t run. 🧹 Code Linters & Static Analysis → Catch issues before you ever hit “merge.” 🚫 CI/CD Pipeline Guards → Fail early if the code doesn’t meet the bar. 🖱️ Disable Buttons Until Fields Are Filled → A tiny UX tweak = huge bug savings. But here’s the real lesson: Poka-Yoke isn’t just a tactic. It’s a mindset shift. From reactive QA → to proactive quality engineering. 💬 Your turn— Where could a little mistake-proofing save you a massive headache in the future? #SoftwareTesting #QualityEngineering #Pokayoke #TestMetry

Transform Your QA Strategy with Shift Left for Quality Product Delivery !! Delivering high-quality products efficiently remains a significant challenge in software development. Traditional methods often push testing to the end of the development phase, leading to delays, increased costs, and higher risk of defects. Shift Left advocates for integration of testing earlier in the development lifecycle. Instead of waiting for the development phase to conclude before initiating testing, Shift Left encourages testing activities to begin as early as the requirement and design stages. Lets see some of the benefits of using Shift left in your projects: 1. Early Detection of Defects and Faster Feedback: Identifying bugs and issues at the initial stages of development is significantly more efficient than catching them later. This leads to shorter feedback loops resulting in quicker and cheaper fixes. 2. Improved Collaboration: By involving QA early, teams can ensure that quality is a shared responsibility, leading to better communication and a unified approach to problem-solving. 3. Cost Efficiency: The cost of fixing a defect increases exponentially as it progresses through the development stages. 4. Faster Time-to-Market: As the defects are identified and resolved earlier, the development process becomes more streamlined and enabling faster delivery of high-quality products to market. Now lets see few ways to implement shift left into our projects: 1. Automate as much as possible: Automated tests can be run early and often, providing immediate feedback thereby reducing the time and effort required for manual testing. Improve the unit test coverage, integration tests, contract test, end to end regression tests. 2. Exploratory Testing: Exploratory testing complements automated testing by identifying issues that automated tests might miss. It involves testers exploring the application, simulating real-world scenarios, and identifying potential issues. 3. Test-Driven Development (TDD): TDD encourages us to write tests before code, ensuring that testing is an integral part of the development process. 4. Continuous Integration / Continuous Delivery (CI/CD): CI/CD pipelines ensures that code changes are continuously integrated and tested. This helps in early detection of defects and accelerates the delivery process. "Early bug detection is not just a task; it's a mindset." What challenges have you faced in shifting left, and how have you overcome them? Share your experiences and insights in the comments below!! Your feedback could help others on their journey to improved quality and faster delivery.