Tips for Optimizing App Performance Testing

Stop guessing why your app is slow. Open DevTools and measure. Open the Performance tab, hit Record, and use your app: click, scroll, navigate, interact. Then stop the recording and inspect the timeline. You're looking for: - Long tasks (over 50ms) - Reflows and repaints - Scripts blocking the main thread These are the red flags. If you’re blocking rendering or constantly forcing layout recalculations, your app’s going to feel janky. Next, dive into the Flame Graph. Wide bars = expensive functions. These are your hotspots. Refactor them. Avoid blocking the UI. Break them into smaller, async chunks where you can. If it’s heavy and synchronous, it’s a problem. DOM reads and writes? Batch them. Reading layout properties like offsetWidth forces the browser to recalculate layout. Writing right after that (e.g., changing styles) forces it again. That’s a double reflow, and it adds up fast. To fix it group reads first, then writes.Trigger one reflow, not two. Memory usage matters too. If you haven’t taken a Heap Snapshot, you have no idea what’s lingering in memory. Detached DOM nodes, closures, abandoned observers, they all pile up over time. Leaks don’t crash your app, they slowly choke it. Long JavaScript tasks freeze the UI. If you're blocking the main thread for more than 50ms, you're locking out user interaction. Break those tasks apart: - Use setTimeout, requestIdleCallback, or Web Workers - Prioritize responsiveness over raw throughput Don’t load everything up front. If your initial bundle includes every image, every script, every component—you're forcing the browser to choke before it can render anything useful. To fix it - Lazy-load non-critical assets - Use for essentials like fonts - Defer anything not needed for first paint DevTools isn’t optional, it’s a daily tool. You don't fix performance by guessing. You fix it by measuring.

AI Architect & AI Engineer | Building Agentic Systems & Scalable AI Solutions

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

Senior Flutter Developer - (Android/iOS)

🚩 A Flutter debug flag I wish I had known earlier. Just one line before 𝗿𝘂𝗻𝗔𝗽𝗽(): import 'package:flutter/rendering.dart'; void main() {  debugRepaintRainbowEnabled = true; // 👈  runApp(const MyApp()); } Turn it on and Flutter visually highlights every repaint with a rainbow flash. Simple, but very effective for performance debugging. Most Flutter devs focus on rebuilds (const widgets, splitting trees, state management). But 𝗿𝗲𝗯𝘂𝗶𝗹𝗱𝘀 ≠ 𝗿𝗲𝗽𝗮𝗶𝗻𝘁𝘀. A widget can stop rebuilding and still repaint frequently at the GPU level, silently affecting performance. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗳𝗹𝗮𝗴 𝘀𝗵𝗼𝘄𝘀: Each repaint = flash Occasional flashes = normal (animations, scrolling) Constant / large-area flashing = potential unnecessary work 𝗥𝗲𝗮𝗹 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: A background gradient was repainted on every scroll, even though nothing visually changed. UI looked fine, GPU wasn’t. 𝗤𝘂𝗶𝗰𝗸 𝗳𝗶𝘅 𝘁𝗼𝗼𝗹: RepaintBoundary(  child: YourWidget(), ) Use it to isolate repaint-heavy regions, not the whole app. 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄: Enable flag → interact → spot repaint hotspots → isolate → remove flag. It shifts how you debug Flutter performance. Have you ever caught a repaint issue early, or only after it caused jank in production? #Flutter #FlutterDev #FlutterPerformance #MobileDev #Dart #PerformanceOptimization #SoftwareEngineering

I’m a software/AI engineer, husband, dad, and Christian trying to make my family harder to overwhelm in the AI age.

Want your XR app to have the best user experience? Performance monitoring tools are key to identifying bottlenecks & optimizing performance. Here's how to leverage them effectively 🧵 1/ First, establish KPIs to track for your XR app. Frame rate, GPU utilization, memory usage, load times are all critical metrics. The right tool will monitor these in real-time as users interact with your app. 2/ For VR, aim for a stable 90 FPS to avoid motion sickness. AR apps should target 60 FPS. Monitor frame rates under various conditions (low power mode, heavy usage) to gauge real-world performance. Tools like Intel GPA are ideal for this. 3/ GPU utilization is another key metric, especially for graphics-heavy XR apps. You want the GPU working hard but not constantly maxed out. Tools like Unity Profiler or Unreal Insights identify GPU-intensive areas to optimize. 4/ Memory management is crucial in XR to avoid crashes & stutters. Track memory usage/leaks over time with tools like Visual Studio or Xcode. Look for assets/areas using excessive memory and optimize resource loading. 5/ Don't forget to monitor load times, especially for asset-rich XR apps. Use profiling tools to see what's causing long loads - large textures, unoptimized models, too many objects, etc. Optimize based on these insights. 6/ Regularly test on a range of devices to gauge real-world performance. Automated performance tests help identify regressions. Many tools can test XR apps on farms of physical devices for comprehensive insights. 7/ Lastly, don't just rely on tools - actively seek user feedback on app performance. Prompt users to report any slowdowns, stutters, or instability they encounter. Combine this qualitative data with quantitative metrics for the full picture. 8/ Optimization is a pain and a half. But, the upfront effort pays dividends in user experience and engagement. Work on it until no-one mentions stutters or frame drops.

Senior React Native Engineer & Tech Lead · iOS · Android · TypeScript · Web3/Blockchain

Improve React Native App Performance I am sharing my years of React Native experience in a single post. 𝗦𝗮𝘃𝗲 𝗶𝘁 𝗳𝗼𝗿 𝗹𝗮𝘁𝗲𝗿, and evaluate your project against these practices! 𝗖𝗼𝗱𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝟭. Don't rely on useMemo & useCallback too much, try to design your components in a way that minimizes the need for them 𝟮. Don't rely on custom hooks too much, custom hooks improve readability, not performance. Each instance uses separate resources 𝟯. Keep files small (~200 lines, including stylesheet in it) 𝟰. Follow SOLID (Single Responsibility), components should get data directly instead of props. 𝟱. Use useRef instead of useState if it doesn't reflect on the UI 𝟲. Avoid inline styles, use StyleSheet 𝗥𝗲𝗻𝗱𝗲𝗿𝗶𝗻𝗴 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝟳. Avoid calculations inside JSX, process data before rendering. Bad: Output: {calculateSomething()} Good (value from store): Output: {preCalculatedValue} 𝟴. Lazy load Bottom/Top tabs to render only when needed. 𝟵. If a component is off-screen in a scroll view, don’t render it until user scroll down (use scrollHandler) 𝟭𝟬. Use debounce to prevent extra renders & API calls 𝟭𝟭. Use InteractionManager to defer non-essential tasks during animations 𝟭𝟮. Use Fragments (<>...) instead of unnecessary wrappers to reduce the component tree depth 𝟭𝟯. If using a horizontal FlatList with 20 items, but only 2 items are visible and paging is enabled, optimize it by: maxToRenderPerBatch={5} initialNumToRender={2} windowSize={3} removeClippedSubviews={true} 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 & 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: 𝟭𝟰. Use FastImage 𝟭𝟱. Use FlashList instead of FlatList for better performance with large lists 𝟭𝟲. Use Reanimated instead of Animated for animations 𝟭𝟳. If using local images, optimize them using any online tool to reduce size without visible quality loss, this improves load time and prevents FPS drops 𝟭𝟴. Avoid racing conditions (calling the same API multiple times), take the latest value 𝟭𝟵. If calling multiple APIs, use Promise.allSettled() instead of sequential await calls Bad: (3 + 5 = 8 seconds) await getData() 3 seconds await getDataTwo() 5 seconds Good: (5 seconds) Promise.allSettled([getData(), getDataTwo()]) 𝟮𝟬. Reduce bundle size by removing unnecessary dependencies & dead code. 𝟮𝟭 Reduce API over-fetching using pagination & loading data in chunks. 𝟮𝟮. Instead of waiting for the API response, update the UI immediately when the user taps the "Favorite" button. If the API call fails, revert the change. 𝟮𝟯. show cached data immediately while fetching new data in the background. ♻️ Repost this to help other developers  Feel free to ask any questions about this post or any other React Native topic. 👍 #react #reactnative #javascript #typescript #developer #lead #android #ios #senior #mobile #Tips #technical #remote #CareerGrowth

Founder | Engineering Leader | Author & Speaker

🏎️🏁 Google just dropped the performance tips talk from Google I/O earlier this year, that highlighted our team's work around the app performance! In my experience, a lot of performance improvements come from simply getting your house in order and starting to pay attention to performance as part of your daily work. It's easy to start. Update those old libraries because the new ones tend to have stability and performance improvements. Updates introducing performance regressions? Report them to the maintainers and advocate for fixes. Remove old dependencies because they're usually holding you back and bloating your apps, and complicating your migrations and adoption of new tech and better tooling. Take advantage of the capabilities of the tools and frameworks you already use, like R8 capabilities. Add a baseline profile for a core user journey. Measure those changes with simple benchmarking libraries. Google Play / Firebase observability is more than enough to get you started with good decision making. You don't need anything particularly fancy - just a willingness to look at the data and act on it. Finally, maintain your gains by testing and safeguarding against regressions. Address those regressions promptly, before they get baked into your stack. Check out the talk here: https://lnkd.in/ehpqv8mP #android #performance #gigadrew #googleio

Forward-Deployed CEO | Building Thoughtful Testing Systems for Companies and Testers | Co-Founder @ MuukTest (Techstars ’20)

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?

Microsoft MVP (Business Applications) | Founder, LowCodePower | AI Governance | Copilot | Microsoft Certified Trainer | Speaker

Testing is a crucial part of Canvas App development in Power Apps. Here are some useful tools and techniques for testing your Canvas Apps effectively: 1. Built-in Power Apps Tools 🛠 Monitor Tool Tracks all events, performance, and data calls in real time. Helps debug API calls, errors, and performance bottlenecks. Access via: Power Apps Studio → Advanced Tools → Monitor 🛠 Test Studio (Automated Testing) Allows creation of automated UI tests using test cases. Helps validate app behavior and prevent regressions. Access via: Power Apps Studio → Advanced Tools → Test (Experimental) 🛠 Performance Checker Identifies performance issues related to controls, formulas, and data sources. Provides recommendations for optimization. Access via: File → Settings → Performance Checker 🛠 Preview & Live Testing Run your app in preview mode to simulate real-world interactions. Test different devices (desktop, tablet, mobile) for responsiveness. 2. External Testing Tools 🔍 Browser Developer Tools (Chrome, Edge, Firefox) Use F12 DevTools to inspect network requests, console logs, and performance. Useful for debugging Power Apps Web Player issues. 📱 Device Emulators & Real Devices Test your app on iOS & Android using Power Apps mobile app. Use Browser DevTools’ mobile view to simulate different screen sizes. ⚡ Power Automate Automate UI testing by triggering Power Automate flows. Useful for testing workflows, approvals, and notifications. 3. Best Practices for Testing ✅ Unit Testing – Test individual formulas, variables, and logic separately. ✅ Integration Testing – Verify how the app interacts with external data sources like SharePoint, Dataverse, SQL. ✅ User Acceptance Testing (UAT) – Involve end-users to validate app functionality before deployment. ✅ Security Testing – Test role-based access, authentication, and data security settings. What are some effective testing strategies for you? #powerapps #canvasapps #powerappstesting #powerappstips #powerplatform

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