Ever spent hours debugging code only to find a tiny mistake? 👀 Unit tests catch errors early, improve code quality, and save time. From Python to JavaScript, our latest guide shows practical examples, best practices, and common pitfalls. Read the full article 👉🏻 https://lnkd.in/dKTydXwH 💬 What’s your biggest challenge with unit testing? #UnitTesting #SoftwareDevelopment #CleanCode #TestPapas
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𝗦𝘁𝗼𝗽 𝗺𝗮𝗻𝘂𝗮𝗹 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴. 𝗦𝘁𝗮𝗿𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹. 🚀 I used to test my code by manually typing inputs into the terminal and "hoping" I didn't miss anything. It was exhausting, prone to human error, and simply didn't scale. In Part 5 of my Python Essentials series, I’m sharing how I moved from manual checks to Unit Testing, the professional way to ensure your code survives every update. 𝗧𝗵𝗲 𝟯-𝗦𝘁𝗲𝗽 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 "𝗠𝗮𝗰𝗵𝗶𝗻𝗲" 𝗠𝗶𝗻𝗱𝘀𝗲𝘁: I treat every function as a unit. Input goes in, output comes out. A unit test is simply the automatic check that confirms the output is correct every single time. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗮𝘀𝘀𝗲𝗿𝘁: I started using the 𝗮𝘀𝘀𝗲𝗿𝘁 keyword to define exactly what the result should be. If my logic fails, Python catches the bug instantly. 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗽𝘆𝘁𝗲𝘀𝘁: Instead of writing massive lines of manual code, I integrated 𝗣𝘆𝘁𝗲𝘀𝘁. It handles the heavy lifting of condition checks and reporting, allowing me to focus on building features instead of firefighting bugs. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Whether you are building a Web App or an Automation Script, unit tests give you the "defensive" shield you need to move forward confidently. You catch edge cases early, protect your business logic, and build professional coding habits from Day One. Are you still testing manually, or is it time to automate your QC? 🔗 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗮𝗻𝗱 𝘀𝗲𝗲 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲 𝘀𝗻𝗶𝗽𝗽𝗲𝘁𝘀 𝗵𝗲𝗿𝗲: https://lnkd.in/dWc9BQKf #Python #Pytest #SoftwareEngineering #UnitTesting #CodingTips #CleanCode #Automation #WebDevelopment #VAULT
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🚀 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐍𝐚𝐦𝐢𝐧𝐠 𝐂𝐨𝐧𝐯𝐞𝐧𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 🚀 Consistency in naming isn’t just style—it’s clarity, collaboration, and long-term success. Here are some of the most common conventions every developer should know: 🔹 𝐂𝐚𝐦𝐞𝐥 𝐂𝐚𝐬𝐞 👉 Example: firstName 💡 Usage: Variables & functions 🔹 𝐏𝐚𝐬𝐜𝐚𝐥 𝐂𝐚𝐬𝐞 👉 Example: UserAccount 💡 Usage: Classes, interfaces, enums 🔹 𝐒𝐧𝐚𝐤𝐞 𝐂𝐚𝐬𝐞 👉 Example: user_account 💡 Usage: Python variables, DB fields 🔹 𝐊𝐞𝐛𝐚𝐛 𝐂𝐚𝐬𝐞 👉 Example: user-account 💡 Usage: URLs, CSS classes 🔹 𝐒𝐜𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐂𝐚𝐬𝐞 👉 Example: MAX_SIZE 💡 Usage: Constants 🔹 𝐇𝐮𝐧𝐠𝐚𝐫𝐢𝐚𝐧 𝐍𝐨𝐭𝐚𝐭𝐢𝐨𝐧 👉 Example: strFirstName, arrUserNames 💡 Usage: Legacy C/C++ style ✨ 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 1. Stay consistent across your codebase 2. Use clear, descriptive names 3. Follow language-specific guidelines (PEP 8, Java conventions, etc.) 𝘎𝘰𝘰𝘥 𝘯𝘢𝘮𝘪𝘯𝘨 = 𝘳𝘦𝘢𝘥𝘢𝘣𝘭𝘦, 𝘮𝘢𝘪𝘯𝘵𝘢𝘪𝘯𝘢𝘣𝘭𝘦, 𝘢𝘯𝘥 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦 𝘤𝘰𝘥𝘦. 🧑💻 #Programming #CodeQuality #BestPractices #SoftwareDevelopment #CleanCode #DevTips
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I've written both horizontal and vertical C# in production for years. Here's where I landed. Horizontal style: - Feels faster to write - Looks "professional" and dense - Works fine for trivial expressions - Becomes unreadable past 100 characters - Hides bugs in plain sight (I've personally missed 3 wrong-argument bugs in horizontal code) Vertical style: - Takes 2 more seconds to write - Looks "obvious" — almost too simple - Every operation is independently scannable - Diffs are cleaner (one change = one line in git) - Code review speed increases dramatically My default: vertical. Always. But I switch to horizontal when the expression is genuinely simple — a null coalesce, a basic ternary, a single-method LINQ call. The deciding rule I use: if I need to move my eyes horizontally more than once to parse a line, it goes vertical. This is based on refactoring 100+ .NET services and tracking PR review times before and after. Our average review time dropped by roughly 30% after adopting vertical formatting standards. I share more comparison breakdowns like this in my newsletter: https://lnkd.in/g-2uVCWz #dotnet #csharp #cleancode #codequality #programming #softwaredevelopment #codereview
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One testing pattern I like using when dealing with multiple edge cases is the table driven test approach. You can think of it almost like a for loop for tests. So instead of writing many separate tests for every possible input, all you have to do is just define a small table that contains different inputs and their expected results. Then you run the same test logic against each cases. This keeps the tests shorter and makes the edge cases very easy to see. So when someone reads the test, they can quickly understand all the scenarios the code is expected to handle. It also makes the tests easier to maintain, so that whenever a new edge case appears, we can simply add another row to the table instead of creating a completely new test for it. When I structure tests like this, I will still keep the AAA pattern in mind. #softwareengineering #unittesting #testing #jest #reactnative #javascript #typescript #testautomation #cleanCode #softwaredevelopment #qualityengineering #devbestpractices #coding #programming
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Turn your test design into repeatable automation quality. 📈 I’ve spent months refining a dedicated Playwright + C# (MSTest) assistant. This isn't a general-purpose bot; it's a private expert designed for structured, maintainable testing. What makes it different? Strict C# Focus: Zero JavaScript or Python—just pure .NET conventions. Traceability: It generates companion documentation (Word or JSON) for every test flow. Real-World Logic: It uses your HTML evidence to discover locators accurately. Whether you’re onboarding new testers or scaling an enterprise framework, this tool ensures your code remains clean and standards-compliant. CTA: Check out the Custom GPT: https://lnkd.in/euUCJeZx #Playwright #CSharp #DotNet #AutomationTesting #QualityAssurance #MSTest #CustomGPT #AIforQA
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😂 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝘁𝘄𝗼 𝗲𝗿𝗮𝘀, 𝘀𝗮𝗺𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: 𝗼𝘃𝗲𝗿𝗰𝗼𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗻𝗴 𝘀𝗶𝗺𝗽𝗹𝗲 𝗹𝗼𝗴𝗶𝗰 2020: solve a basic odd/even check with a giant chain of if statements 😅 2026: wrap a tiny problem in an unnecessary abstraction / helper call 📦💀 Different style… same issue: using too much for something simple. Real lesson (and it matters) 👇 Good programming is not about writing more code. It’s not about using more libraries either. It’s about: ✅ knowing the simplest correct solution ✅ understanding built-in language features ✅ using libraries when they solve a real problem (not a 1-line one) ✅ keeping code readable, testable, and easy to maintain Libraries are powerful. 🛠️ But engineering judgment is knowing when not to add one. Sometimes the best code is: ◆ smaller ◆ boring ◆ obvious ◆ and done ✅ #Programming #SoftwareEngineering #DeveloperHumor #CleanCode #JavaScript #Coding #ProblemSolving #DeveloperMindset #WebDevelopment
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Code reviews should be about logic and architecture, not about your teammate’s missing semi-colons or messy indentation. We’ve all been there: You open a Pull Request and it’s a sea of red and green changes. Not because the code changed, but because the developer's IDE auto-formatted 50 files. Improper indentation and "cowboy formatting" aren't just ugly; they are a tax on your team’s productivity. If your team is wasting time commenting "please fix indentation" during a review, your process is broken. The solution? Stop relying on human discipline and start using Git Hooks. I implement node Husky, python pre-commit on every project I lead. The workflow is simple: You write your code. You run git commit. Husky triggers a pre-commit hook that runs your formatter (Prettier, Pint, or ESLint). If the code isn't perfect, it formats it automatically or rejects the commit. The result: The codebase stays consistent, the "format wars" end, and your PRs stay focused on what actually matters—the business logic. Software engineering is about automation. If you can automate your code style, why wouldn't you? How is your team handling code consistency? Are you using Husky, or still manually fixing tabs vs. spaces? #DeveloperProductivity #CleanCode #SoftwareEngineering #Husky #GitHooks #WebDevelopment
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Wrapped up my REST API automation project! Built an automated test suite for the Restful-Booker API covering all CRUD operations and authentication flows. What I implemented: - 11 automated API tests with full coverage - Authentication token management - Request/response validation - Data-driven testing approach - Professional documentation with examples Stack: Python, Requests, Pytest Took about one week. The tricky part was handling auth tokens across tests - learned a lot about session management and fixtures. This project helped me understand the difference between testing APIs vs testing UIs. https://lnkd.in/eN7S_vRY #QA #APITesting #Python #Automation #SoftwareEngineer #RestAPI
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This playlist is a great resource for anyone who is a beginner in software testing. I’d like to highlight that it’s especially useful for those who are just starting their journey in test automation. Automation with playwright. https://lnkd.in/epwUJPZi
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The Java inference engine deliverance (https://lnkd.in/en-BAHPS) has its 3rd release. New features: stop_tokens support (save computer to stop at user defined keywords maven vibe plugin https://lnkd.in/e_AxVwDB support for semantic search with the #mongo leaf model other stuff I forgot! #java #ai
Maven vibrant plugin for vibe coding and spec driven development
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