Early

Your code coverage is 80%? Congrats… but your tests might still be useless. For years, code coverage has been the go-to metric for testing. The idea? The more lines of code covered, the better your tests. But here’s the problem: 🚨 90% code coverage does not mean high-quality tests. 🚨 0% code coverage doesn’t necessarily mean bad code, just that if you break it you won't have a clue. I’ve seen teams aim for high coverage, only to realize their tests weren’t actually catching or preventing real bugs. So what should we really measure? ✅ Do your tests actually catch and prevent bugs? ✅ Do they cover real-world use cases—both happy paths and edge cases? ✅ Can your tests detect unexpected mutations or regressions? Here is our attempt to define a new way to measure the tests quality of the tests themselves: 🔥 EQS (Early Quality Score) 🔥 Instead of just checking how much of your code is covered, EQS factors in test quality with three key dimensions: Code Coverage – What % of your code is tested? Mutation Score – How well do your tests detect real code changes? Scope Coverage – What percentage of your public methods have unit tests and 100% coverage? This takes test quality to the next level, answering the real question: Are my tests actually protecting my code? We’ve been using EQS internally at Early, and the insights are game-changing. It helps us evaluate our technology for high-quality test generation, spot gaps, and improve test effectiveness. What are your thoughts? Do you have other ideas to measure the quality of the tests themselves?

How can you tell if your tests are really protecting your code? Last week we wrote about EQS (Early Quality Score) which provides a clear indication of whether your tests are actually protecting your code. ❕ Reminder: EQS score 🟰 Code Coverage ❎ Mutation Score ❎ Scope Coverage (Scope coverage = how much of your methods have unit tests with 100% coverage) Today, we’ll do a deep dive into the second component – Mutation Score which evaluates whether your existing tests are effective = able to identify defects in your code. Mutation testing works by introducing small changes ("mutants") to the source code and then running the existing tests to see if they detect the changes: 1️⃣ Generate mutants - create multiple versions of the original code each with a slight modification (for example, changing a logical or mathematical operator and altering a constant value or a conditional boundary) 2️⃣ Run tests on the mutated code - test each mutant using the existing test suite to see whether the changes are detected (i.e., cause the tests to fail). Detected means killed, not detected means survived. 3️⃣ Analyze the results - Killed mutants vs survived mutants. There are other types of “not killed” mutants, like no coverage, timeouts, and errors which are beyond our scope today. 4️⃣ Calculate the mutation score - the simplified formula is ((killed mutants + time-out mutants) / (all mutants – error mutants)) * 100 💡 Important: High code coverage does not always result in a high mutation score! 💡 This is just one reason why you shouldn't rely on code coverage as the only way to evaluate the effectiveness of your tests. 💡 But using code coverage + mutation score is not enough either, which is why Early has completely rethought the way we approach test quality with EQS, and why you should too!   To review our complete analysis together with specific test examples, read the full blog post 👉 link in the first comment

Ready to supercharge your code quality in minutes? Over the past several months, thousands of developers have put their trust in EarlyAI as their personal AI test engineer. ⭐ Lines of code analyzed: 4M ⭐ Unit tests Generated: 130,000 ⭐ Development man-months saved: 109.4 One of the things developers keep raving about is how easy and valuable it is to use For example, to use EarlyAI in VS Code all you have to do is: 1.       Install from the marketplace and verify. 2.      Sign in to the EarlyAI extension (email/Google/GitHub). 3.      Generate tests with the “magic wand” in the side bar, the code lens above the method name, or the context menu. And that’s it! Within minutes you’ll have: 💡 Comprehensive, functional and high-quality tests. 💡 Tests that include mocks, happy paths and edge cases. 💡 Automated documentation for easy review. 💡 Improved code coverage, also at the method level (for supported frameworks). 💡 All directly within your favorite IDE. Save time, boost code quality, reduce bugs, accelerate development, and much more! Why not try it out today? For more information including detailed instructions, visit our documentation 👉 Link in first comment below

Code coverage ≠ Code quality --- what should you measure instead? ⭐ Do your tests help you catch or prevent bugs? ⭐ Do your tests cover all use cases, including happy paths and edge cases? Today, the standard way to measure testing is by code coverage - the percentage of the code that is covered by tests. 0% coverage = no or useless tests 100% coverage ≠ high quality (low test quality, do not cover enough cases of different input datasets, only public methods, etc.) The problem with code coverage is that it cannot answer the important questions above. 🎯 Early is proud to introduce a better way to measure testing! The *EQS (Early Quality Score) number* indicates the overall quality of the tests for that file or project. EQS score 🟰 Code Coverage (percentage of code covered by tests) ❎ Mutation Score (the ability of the test to identify mutations/bugs in the code) ❎ Scope Coverage (percentage of public methods with unit tests that cover 100% of their respective method) EQS takes test quality measurement to the next level by providing an answer to what you really want to know: ⭐ Are my tests really protecting my code? Read the full blog post to learn in depth why EQS is better than using code coverage alone. See how Early uses EQS to test its technology and produce high quality working tests, including many examples and use cases. 👉 link in the first comment

Using AI for coding without also using AI for testing? That’s like writing without proofreading. GitHub Copilot and Cursor are both highly popular tools that provide excellent general-purpose AI assistants for code generation and an awesome developer experience. The problem is that when it comes to testing, they only provide you with incremental test generation that require a lot of developer effort to make them work. In contrast, AI agents that were trained for test code can provide you with 100s of working unit tests in 5 minutes. Tests that protect you from future bugs and tests that actually find bugs in your code. So which should you use? You need to use both. Your favorite AI code gen assistant ➕ An AI agent designed for test code generation ⏩ The effective way to maximize both code quality and speed of development. Want to learn more? Read our blog post which provides a much more detailed comparison between AI code gen assistants and AI test code agents Extra credit: Read our CEO’s take on the evolution from AI Assistants to AI Agents 👉 Links in the first comment

Think code coverage is just a number? Think again! You may know what code coverage is, but why does it matter, and how do you measure it? For those starting from scratch, code coverage measures how much of your codebase is executed during testing. But it’s more than just numbers—it’s about building confidence in your code’s quality: ⭐ Catch bugs before they reach production ⭐ Improve code reliability and maintainability ⭐ Identify untested and risky parts of your code So, which code coverage metrics should you track? 🔍 Statement Coverage – Was every individual statement executed? 🔍 Branch Coverage – Are all if/else branches tested? 🔍 Function Coverage – Did every function/method get called? 🔍 Line Coverage – Are your tests executing all lines of code? But here’s the catch: 💡 100% code coverage is not the goal! While high coverage is useful, getting to 100% takes excessive time and effort—and doesn’t guarantee bug-free code. Instead, the focus should be on smart coverage, ensuring meaningful tests rather than just hitting a percentage. The solution? Simplify the process! ✅ Generate comprehensive unit tests automatically. ✅ Cover happy paths, edge cases, and risky areas for in-depth insights. ✅ Track coverage metrics in real time, without the manual effort. Would you like to achieve high code coverage with minimal effort? Try EarlyAI – The Agentic Test Engineer today! And how would you test the tests themselves? Use mutation testing—but that’s for another post. 😉

🛑 Stop letting bugs slip through the cracks! One of our recent blog posts featured a tutorial video by open-source advocate Eddie Jaoude, where he showed how automated unit test generation helped him quickly catch bugs that manual testing missed. Now, our latest blog post breaks down the process in detail ✅ How It Works: ➡️ Install the IDE extension ➡️ Set up the configuration to match your test framework ➡️ Generate unit tests ➡️ Add documentation automatically ➡️ Run the tests and catch bugs ➡️ Implement improvement suggestions ➡️ Rerun the tests to verify fixes 💡 The post includes real code examples so you can learn how to spot subtle bugs that often go unnoticed in manual testing. 💡 Highly recommended whether you usually skip writing tests due to lack of time or want to streamline your entire testing process. 💡 Try it out for yourself—generate tests for all your files in under a minute. Installation is quick and free to start! 🔗 Link to the full blog post in the first comment below

Want to learn the secret to bug-free code? Yes, we know. You hate having to manually write and run tests on your code. We fully empathize with your pain 😰 Most developers hate testing, yet they still spend nearly a third of their time on code quality related issues! But doing testing right really is the best path to ensuring your code has little to no bugs. So here's some good news ---it doesn’t have to be as difficult, annoying or time-consuming as you think! Let’s start with the first step. You need to run unit tests which validate the smallest testable parts - functions or methods of a codebase – ensuring that they all work as expected. 🌟 The benefits of unit testing include: ·     Early bug detection (shift left) ·     Support for Continuous Integration ·     Faster debugging and resolution ·     Improved code design and modularity ·     Protection against regression 💪Top 5 unit test best practices: 1.       Focus on test isolation for reliable results 2.       Test both positive and negative scenarios 3.       Follow the Arrange, Act, Assert (AAA) pattern 4.       Name your tests clearly for better readability 5.       Keep unit tests small, focused and maintainable 🚩 What NOT to do when running unit tests: ·     Skip edge cases ·     Test too much at once ·     Use hard-coded values ·     Use inconsistent test names 💡 Pro tip: Wouldn't you like to have an AI agent handle all the tedious parts of testing your code? Test smarter with Earl – your friendly neighborhood AI test engineer! Want to learn more about unit testing? Read our full blog on unit testing best practices 👉 (link in the first comment below).

🚨 This video will transform the way you test and debug your code 🚨 *Trigger warning*: This video will, without a doubt, make you regret all those many boring hours you spent manually writing tests while trying to debug your code. Writing unit tests is rarely a priority for developers. You either don’t have time or there are other tasks you would **much** rather be doing. But you also know that it will most certainly improve the quality of your code. So, what's a developer to do? This is where EarlyAI comes to the rescue! 💡 Eddie Jaude, the OSS advocate, took EarlyAI for a spin and was blown away by how, in just a few minutes, it generated unit tests and immediately found bugs that were missed with manual testing. 🔥 Best of all - EarlyAI is free for OSS developers so why not give your project a major boost? Try it out today --- 30 unit tests in less than one minute! Link in first comment 👇

How do you distill a year of hard work into just 60 seconds? As a startup CEO, storytelling is the ultimate skill. Being a startup CEO is one of the toughest - and most enjoyable - professional journeys I’ve ever had. You have to be a storyteller, product manager, sales lead, marketing lead, operator, interviewer, cheerleader - and juggle 10–50 balls in the air every day. But of all these roles, the storyteller is possibly the most important. You need to tell your story clearly and concisely to employees, customers, investors, and even your grandma - and be able to do it in 60 seconds. On the first week of the ICON - Israel Collaboration Network program, we learned the structure of a great startup story: 1️⃣ Team – Why you’re the best team for the job 2️⃣ Problem – What pain are you solving? 3️⃣ Why now? – Why is this pain becoming unbearable? 4️⃣ Your solution – How amazing and hard to replicate your solution is 5️⃣ Your differentiation – Why is your solution unique and better than everything else? 6️⃣ Your market – Ideally, a half-a-trillion-dollar one 🙂 Now imagine telling this story in 5 minutes, which is already challenging - and then condensing it into 1 minute (!) As part of the ICON program, we honed our storytelling skills. In the first week, we condensed our stories into a 5-minute pitch deck, and presented it during the "Grill" session to top-tier VCs for feedback. It was tough but incredibly insightful. The next challenge came in the second week: summarizing everything into 60 seconds and presenting it during dinner and a fireside chat to ICON prestige members (VCs, founders, executives, community members, and friends). We had just a few hours to prepare, and I don’t think anyone felt fully ready - but with the incredible guidance of Donna Griffit, we pulled it off. Everyone did an amazing job. Here’s my 60-second pitch: summarizing my life’s work from the past year into 60 seconds . Do you now understand what I am working on 🙂 ? 🚀Asaf Yanai Koby Berkovich Or Israel Ofir Israel David Talinovsky Gil Perry Maayan Salom Or Avidov Elad Schulman Lior Ziv Ely Abramovitch Dor Vardi Yehuda Niv Nir Kenner Nir Gazit Gal Kleinman Yasmin Dunsky Batch 16 feel free to add links to your session for others to learn about what your companies.