Improving Async Thread Management with Executors in Android

Modernising Legacy Systems with Understand from Scitools Modernising legacy code, without rewriting everything from scratch We’ve all been there: an old system that’s grown over the years, and nobody fully understands it anymore. And now it’s supposed to be modernised ‘in a jiffy’.   The problem is: you can’t refactor something you don’t understand. And with legacy systems, ‘understanding’ is the hardest part. SciTools’ Understand is designed precisely for this purpose: o Dependency Maps: Visualises what actually depends on what o Call Trees & Control Flow: Clarity instead of trial and error o Metrics Dashboard: Identify hotspots where refactoring is most effective o Architecture Checks: Spot violations of layer structure immediately o Multi-Language: C, C++, Java, C#, Python and more in a single tool The best bit: you don’t need to understand the code to make sense of it. Understand makes the structure visible: and suddenly patterns become apparent that were previously hidden.   Typical workflow: 1. Import code → Generate dependency graph 2. Analyse cyclomatic complexity and coupling 3. Identify high-risk areas 4. Refactor in a targeted manner rather than ‘starting from scratch’   Why this is important for modernisation: You don’t refactor blindly. You prioritise based on risk. You identify ‘hotspots’ with high complexity and strong coupling. And, whilst you’re at it, you document what previously existed only in the minds of three people. Real talk: Legacy modernisation isn’t a weekend project. But with the right tools, you can turn “We have to rebuild this from scratch” into “We can modernise this step by step”. Legacy isn’t a fate – just a state of affairs with poor documentation. 😉 Free Trial: www.emenda.com/trial   #LegacyCode #Refactoring #StaticAnalysis #SoftwareEngineering #SciTools #DevTools

One small change that improved my APIs instantly: Stop returning “just data”. Start returning meaningful responses. Before: {   data: [...] } After: {   success: true,   data: [...],   message: "Users fetched successfully" } Why it matters: ✅ Better debugging ✅ Clear frontend handling ✅ Easier logging & monitoring ✅ More predictable systems Good APIs don’t just work. They communicate. Most developers underestimate this. #API #BackendDevelopment #SoftwareDesign #CleanCode #FullStackDeveloper #WebDevelopment #Programming #BestPractices

“Understanding definition ≠ ability to implement cleanly in real code.” This line hit me hard today. We often feel confident after: ✔️ Reading docs ✔️ Watching tutorials ✔️ Understanding concepts But the real test begins when you **write actual code**. 🔍 What I realized I knew `Optional`, `map`, `flatMap` — conceptually clear. But during a simple login implementation: → Fetch user → Extract email I ended up with: ```java Optional<Optional<String>> ``` That moment exposed the gap: 👉 I understood the *definition* 👉 But not the *application* --- ### ⚠️ The Hidden Trap ```java findUserById(id) .map(user -> Optional.ofNullable(user.getEmail())) ``` This creates nesting. Code works… but design becomes messy. ✅ The Clean Way ```java findUserById(id) .flatMap(user -> Optional.ofNullable(user.getEmail())) ``` Now it’s: ✔️ Clean ✔️ Composable ✔️ Readable 🧠 The Real Learning Concepts are just the starting point. Real growth happens when: * You hit confusion * You debug deeply * You refine your thinking 🚀 Takeaway Don’t stop at *“I understand this”* Push until you can say: 👉 *“I can apply this cleanly in production code.”* Because in engineering: Clarity in thinking → Simplicity in code #Java #SpringBoot #CleanCode #BackendDevelopment #LearningJourney

Documenting legacy code automatically Legacy code with no documentation? We’ve all been there. Imagine this: you inherit a codebase of over 50,000 lines. No comments. No architecture docs. And the original developer hasn’t been with the company for years. Sound familiar? The good news is: we don’t have to document everything manually. Tools like SciTools Understand can help us gain insights automatically: ❶Automatic analysis of dependencies and call structures ❷Visualisation of data flows and control flow ❸Metrics for code complexity (Cyclomatic Complexity, Halstead, etc.) ❹ Generation of documentation from the code itself This does not mean that manual documentation becomes obsolete. But it gives us a solid starting point for: • Identifying critical modules • Setting refactoring priorities • Speeding up onboarding for new team members • Cross-language support: C, C++, Java, Python, Ada, Fortran and more ->Experience shows that the combination of automated analysis and human context addition is often the most effective approach. You save time. Time for refactoring, time for testing, time for real improvements instead of manual analysis. Free trial www.emenda.com/trial #SoftwareEngineering #LegacyCode #CodeQuality #DevOps #SoftwareDevelopment #SciTools #Documentation 

Async pitfalls in .𝗡𝗘𝗧 — mistakes that silently hurt your performance Async/await is powerful… but easy to misuse. Here are some common pitfalls I still see in real projects 👇 ❌ Blocking async code var result = service.GetDataAsync().Result; 👉 This can cause deadlocks and block threads ❌ Forgetting to await service.SaveAsync(data); 👉 The task runs without control (and exceptions may be lost) ❌ Mixing sync and async var data = repository.GetData(); // sync await service.ProcessAsync(data); 👉 You lose the benefits of async and hurt scalability ❌ Misusing Task.Run in APIs await Task.Run(() => DoWork()); 👉 In 𝗔𝗦𝗣.𝗡𝗘𝗧 𝗖𝗼𝗿𝗲, this doesn’t make your code “more async” It just moves work to another thread from the same pool This can lead to: • Unnecessary overhead • Thread pool starvation under load 💡 When should you use Task.Run? ✔ CPU-bound work (heavy processing) ❌ I/O operations (DB, HTTP, file system) ❌ Code that is already async 💡 Best practices: • Use async all the way • Always await your tasks • Avoid blocking calls (.Result / .Wait()) • Be intentional with Task.Run Async is not just syntax… it’s about scalability and resource efficiency. Are you sure your async code is really async? 👀 #dotnet #csharp #async #await #aspnetcore #backend #softwareengineering #coding #performance #scalability #bestpractices #developer #programming #tech

Code that works locally is easy. 👉 Code that works in production is engineering. Early in my career, I focused on: ✔ Making features work ✔ Passing test cases But production taught me different lessons: What happens under high traffic? How does your service behave when a dependency fails? Are your logs useful when something breaks at 2 AM? That’s when I started thinking beyond just code. Now I focus on: ✔ Observability (logs, metrics, tracing) ✔ Resilience (retries, timeouts, fallbacks) ✔ Scalability (handling real-world load) 💡 Insight: Writing code is step one. Building production-ready systems is the real skill. #Java #BackendDevelopment #SoftwareEngineering #Microservices #SystemDesign

New tutorial! 🚀 Pytest Tutorial: MLOps Testing, Fixtures, and Locust Load Testing 🧪 Learn how to test, validate, and stress-test ML systems — from unit tests to full API load testing ⚙️ Featuring tools like pytest, FastAPI TestClient, and Locust for real-world performance checks 📊 Built with production-ready workflows, fixtures, CI-style test runners, and automated reports https://pyimg.co/4ztdu 👍 Author: Vikram Singh #MLOps #Pytest #LoadTesting #FastAPI #MachineLearning #SoftwareEngineering #Locust #Python #Testing #AIInfrastructure

Hot take: most performance issues I've seen weren't bugs. They were violations of the Single Responsibility Principle wearing a disguise. Stay with me... When one class does 5 things, it holds 5 things in memory, initializes 5 dependencies, and forces 5 code paths through every request (even when you only needed 1). A few real patterns I've watched play out: - A "UserService" that also handled notifications, audit logging, and report generation. Every login loaded an email client it didn't need. - A controller doing validation, transformation, AND orchestration. Impossible to cache any layer independently. - One giant "helper" class everyone imported; dragging 40 unrelated dependencies into every test and every startup. The fixes weren't fancy. Split the class. Extract an interface. Apply Dependency Inversion so high-level code doesn't drag low-level baggage around. Classic SRP and DIP. Result? Faster startup. Lower memory footprint. Smaller deployable units. Tests that run in seconds instead of minutes. SOLID gets dismissed as 'academic.' But every principle has a performance story hiding underneath. Clean code isn't just for humans. Your runtime reads it too ;) #Java #SpringBoot #SOLID #SoftwareEngineering #BackendDevelopment #designPatterns #systemDesign

Type errors slip through because strict mode is off and any is everywhere. ────────────────────────────── Type Assertions as and satisfies TypeScript catches type mismatches during development before runtime. hashtag#typescript hashtag#assertions hashtag#satisfies hashtag#safety ────────────────────────────── Core Concept TypeScript catches type mismatches during development before runtime. Understanding this concept is essential for writing reliable, maintainable code. It forms the foundation for many advanced patterns you will encounter in production applications. When applied correctly, it improves code readability and reduces bugs during development and maintenance cycles. Key Rules • Use strict mode and avoid any in business logic. • Model API responses with exact interfaces. • Use unknown at boundaries, then narrow deliberately. 💡 Try This type Status = 'open' | 'closed'; function isOpen(s: Status) { return s === 'open'; } console.log(isOpen('open')); ❓ Quick Quiz Q: Why does unexpected behavior occur? A: This usually happens when inputs are not validated or when assumptions about state are incorrect. Always verify the current state before performing operations. 🔑 Key Takeaway Strong typing turns refactors from risky guesswork into confident change. In this guide, you learned the fundamentals of Type Assertions as and satisfies, step by step implementation, best practices, and how to avoid common mistakes. As a next step, try applying these patterns in your own projects and combine them with related concepts. ────────────────────────────── 🔗 Read the full guide with code examples & step-by-step instructions: https://lnkd.in/gT8tETsA

Maestro CI runs were a black box. Every failure, a guessing game. You'd get a JUnit XML. Maybe a screenshot. No history. No pattern. No way to know if this was a new regression or the same flaky selector that broke things last sprint. That changes today. @testrelic/maestro-analytics is now live on npm. TestRelic AI now brings full mobile CI observability to your Maestro test pipelines — the same intelligence layer we built for Playwright, now for iOS and Android flows. Here's what every CI run gets you now: → Unified flow execution history across all your Maestro runs → Flaky flow detection — spotted across runs, not just the latest failure → Ask AI — one click to understand why a flow failed, with YAML command context and prior run history attached → Failure timeline correlated with Amplitude events and Grafana Loki logs → Auto-triage to Jira with full debugging context — no copy-pasting stack traces Works with GitHub Actions, Bitrise, CircleCI, and any CI that runs Maestro flows. Two commands to get started: npm install @testrelic/maestro-analytics npx testrelic-maestro maestro test flows/ Free SDK. No cloud account required to start. Under 5 minutes to your first insight. If your team runs Maestro and you're currently flying blind on CI failures — this is built for you. Drop a comment or DM us if you want the setup guide. #mobiletesting #maestro #testautomation #cicd #qaengineering #mobiledev