Platform Speed and Performance Testing

Last quarter, our team delivered a feature that looked perfect in testing. Users loved the functionality. But within weeks, complaints started pouring in about slow load times and timeouts during peak hours. That's when I realised functional testing alone wasn't enough. Here's what I learned about performance testing as an SDET: Why it matters beyond functional testing: Your code might work perfectly with 10 users. But what happens with 10,000? Performance testing shows you the real story - how your application handles the chaos of peak traffic. I've seen too many teams skip this step. They ship features that work great in staging, then watch them crumble in production. The metrics I track religiously: → Response time (sub-2 seconds keeps users happy) → Throughput (how many requests we can actually handle) → CPU/Memory usage (before the server gives up) → Error rates (the moment things start breaking) My JMeter workflow: Started using JMeter six months ago. Game changer. Set up realistic user scenarios, ramp up load gradually, and get detailed reports that actually make sense to stakeholders. The best part? It plugs right into our CI/CD pipeline. No more "it worked on my machine" excuses. Performance testing isn't glamorous work. But it's the difference between a product that works and a product that works when it matters most. Anyone else dealing with performance issues lately? What tools are working for you? -x-x- JMeter Load Testing & Distributed Performance Testing: https://lnkd.in/g4kxnMBB #SDET #japneetsachdeva

Mastering Real-World App Performance: Our Strategy at Space-O Technologies In the dynamic world of mobile app development, testing and monitoring app performance under real-world conditions is crucial. At Space-O Technologies, we’ve developed a robust approach that ensures our apps not only meet but exceed performance expectations. Here’s how we do it, backed by real data and results. 📊📱 1. Real-User Monitoring (RUM): Our Tactic: We use RUM to gather insights on how our apps perform in real user environments. This has led to a 30% improvement in identifying and resolving user-specific issues. Benefit: By understanding actual user interactions, we've increased user satisfaction rates by 20%. 2. Load Testing in Realistic Conditions: Strategy: We simulate various user conditions, from low network connectivity to high traffic, to ensure our apps can handle real-world stresses. This approach has reduced app downtime by 40%. Outcome: As a result, we've seen a 25% increase in user retention due to improved app reliability. 3. Beta Testing with a Diverse User Base: Method: Our beta testing involves users from various demographics and tech-savviness. This diverse feedback led to a 35% increase in the app’s usability across different user groups. Impact: Enhanced user experience has led to a 15% increase in positive app reviews and ratings. 4. Performance Analytics Tools: Application: We employ advanced analytics tools to continuously monitor app performance metrics. This has helped us in optimizing app features, resulting in a 20% increase in app speed and responsiveness. Advantage: Improved performance metrics have directly contributed to a 30% growth in active daily users. 5. AI-Powered Incident Detection: Innovation: Using AI for incident detection and prediction has been a game-changer, reducing our issue resolution time by 50%. Result: Faster issue resolution has led to a 60% reduction in user complaints related to performance. 6. Regular Updates Based on Performance Data: Practice: We roll out updates based on concrete performance data, which has led to a 40% improvement in feature adoption and efficiency. Return on Investment: This strategic update process has enhanced overall app engagement by 25%. 🔍 Ensuring Peak Performance in the Real World At Space-O Technologies, we’re committed to delivering apps that perform flawlessly in the real world. Our methods are tried and tested, ensuring that our clients’ apps thrive under any condition. If you’re striving for excellence in app performance, let’s connect and share insights! https://lnkd.in/df_Pj6Ps Jasmine Patel , Bhaval Patel, Ankit Shah , Vijayant Das, Priyanka Wadhwani , Amit Patoliya , Yuvrajsinh Vaghela , Asha Kumar - SAFe Agilist #AppPerformance #RealWorldTesting #MobileAppDevelopment #TechInnovation #mobileappdevelopment #mobileapp #mobileappdesign

When we do performance testing, we want to do both mixtures of operations in parallel to understand how the service behaves under anticipated loads, as well as operations insulated in consecutive execution to understand how the individual operations behave. Both types of performance test provide useful information, often the results of the two together explain something not obvious from only one. I saw something this week I have seen many times before. A run of parallel execution, built in anticipation of real-world load, was yielding latencies much higher than target across the board. Even though we were able to isolate the system resources at fault, we couldn't tell if all the operations were having problems, or if one of them was starving resources the others needed. We executed the same set of operations, but one at a time without the others in parallel, so we could get percentile distributions on each one. Only one of the operations was exceeding latency targets, everything else was well within goal. That one operation on its own was using resources the other services needed. That information in hand, we knew where to begin fix investigations. After getting isolated measurements, the next step is investigation, which varies based on what the measurements show. Is it in a front end, a database, CPU, disk, network IO, thread pools, memory utilization, connection pools, or some other resource? What you need to look at is made much simpler when you have the two sets of results guiding you toward further analysis. #softwaretesting #softwaredevelopment #performancetesting Prior articles and cartoons of mine can be found in my book Drawn to Testing, available in Kindle and paperback format. I'm watching how sales of this first go. If it does well, I will collect my newer articles into another edition, so if you like my cartoons and want more, spread the word! https://lnkd.in/gB4NS4BS

Slow is the new downtime. How do you make sure your API won't be slow in production? 𝗟𝗼𝗮𝗱 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Simulate the expected number of concurrent users to understand how it performs under normal and peak loads. Tools: Postman or Apache JMeter. 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Determine how many users your application can handle before performance starts to degrade. Tools: NeoLoad 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗧𝗲𝘀𝘁𝗶𝗻𝗴  Measure the response times under load conditions. It is super important if your applications require real-time responsiveness. Tools: Postman can also help here. 𝗗𝗮𝘁𝗮 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 Populate your testing environment with data volumes that mock what you expect in production. You will understand how data management and database interactions impact performance. Tools: Datagen or Mockaroo. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗼𝗳𝗶𝗹𝗶𝗻𝗴 Set monitoring tools to track application performance metrics. Profiling helps identify memory leaks, long-running queries, and other inefficiencies. Tools: New Relic, Datadog, or Prometheus These 5 things will help you to simulate your production environment. They are not perfect, but they will help you to: - Learn and fix performance bottlenecks early. - Build a reliable API. - Have a more reliable user experience. Are you flying blind or testing like in production?

Performance testing is a crucial aspect of software quality assurance, ensuring applications can handle high loads and perform optimally under stress. Apache JMeter is one of the most powerful tools for load testing, helping QA engineers, developers, and DevOps teams analyze and improve system performance. In my latest guide, I cover: ✅ JMeter Basics – Installation, test plan creation, and components ✅ Thread Groups & Samplers – Simulating user behavior and API testing ✅ Assertions & Listeners – Validating responses and analyzing results ✅ Parameterization & Scripting – Enhancing test efficiency with variables and scripts ✅ Distributed Testing – Scaling tests across multiple machines for real-world scenarios Whether you're new to JMeter or looking to refine your skills, this guide provides step-by-step instructions and best practices to optimize your testing workflow. Are you using JMeter for performance testing? Let’s discuss your challenges and tips in the comments! #PerformanceTesting #JMeter #SoftwareTesting #QA #LoadTesting

💥 Used Postman to simulate High Load: 100 - 200 Requests/Sec on My local machine API testing isn’t just about verifying endpoints—it’s about understanding how your API performs under real-world conditions. Using Postman’s new features, I pushed my API to handle 100 concurrent requests/sec. 🔍 What Happened? 1️⃣ Metrics in Action: Monitored average response times, throughput, and error rates in real-time. 2️⃣ Concurrency Issue: Found primary key violations during simultaneous role creation. 🔧 How I Fixed It Application-Level Concurrency: Leveraged SemaphoreSlim to serialize requests. Database Locks: Implemented row-level locking for secure ID generation. Scalability Enhancements: Explored distributed locks with Redis and Azure Storage. 🚀 Key Takeaways ✅ Postman’s Performance Tab simplifies stress testing. ✅ Addressing concurrency requires tailored solutions for robust systems. ✅ Automation and monitoring are critical for maintaining API reliability under load. 📖 Dive into the Details: Read Full Guide Here: https://lnkd.in/d82maDja #API #PerformanceTesting #Postman #ConcurrencyControl

For a successful 📊 performance test, we simulate the expected usage volume (business metric), measure response times (technical metric), and validate if problem alerting during overload situations (operational metric) works as intended. Technical metrics describe how a system should behave in certain situations. Some examples of technical metrics are: + Response times + Resource utilization + Throughput rate + Error rate + Processing time of batch processes + Software metrics such as code complexity Business metrics describe what a system should support. Some examples of business metrics are: + Number of concurrent users + Number of users authorized to access + Number of transactions under average and peak periods + Service Level Agreement Breach or Compliance + Efficiency of business processes Operational metrics do not directly affect the end-user experience, but in terms of a holistic simulation of production conditions, we include them in our performance test experiments. Some examples of operational metrics are: + Time to start up the application + Time to stop the application + Backup times + Duration of a data restore + Problem detection + Alerting behavior As a performance engineer, you should remember the entire metric family to make your load and performance test successful. I look forward to your questions or comments 😊 #performanceengineering #loadtesting #performancemetrics #performancetesting https://lnkd.in/eaB5rtNk

How fast can your app really go before it starts to crack? Performance testing probes the speed, stability, and scalability of your application under various loads. In a world where users expect instant responses, a sluggish or crashing app can quickly sink your reputation. By simulating real-world traffic patterns, you learn if your infrastructure can handle peak loads—or if you’re one viral post away from a meltdown. Performance testing also uncovers long-running queries, memory leaks, or hidden resource contention. And it’s not just about speed; consistent, predictable performance often separates mediocre user experiences from stellar ones. Don’t wait for complaints—test early to ensure your product can stand up under pressure.