Early

Not all unit tests are created equal. Discover how mocking can transform your testing strategy! Unit tests are a developer’s first line of defense against bugs. They need to check and validate functionality, but even more important than that --- 🎯 They must ensure that your application behaves as expected in real-world scenarios. To do that, they need to uncover edge cases and unexpected behaviors by mimicking real-world scenarios and reliably simulating system behavior. And that’s where realistic and precise mocks come into play! 👉 Our latest technical blog post explores this issue with a detailed example (link in first comment) - testing the checkMaxNumberOfUsersSettings method in a user service. We chose this function because it performs multiple critical tasks and has so many dependencies that testing it requires accurate mocks that ⭐ simulate real behavior ⭐ separate concerns and ⭐ cover edge cases. Read the blog post to learn why you should use advanced mock generation techniques: 💡 Realism without complexity – Align closely with the actual implementation, ensuring tests are both realistic and easy to maintain 💡 Comprehensive coverage - Cover happy paths, edge cases, and error scenarios 💡 Error detection - Easily test error scenarios One of the reasons developers love using EarlyAI is that it automatically generates fully functional unit tests, complete with realistic and maintainable mocks. Try it out for free and generate your first tests within 60 seconds ⚡

The new code generation tools are making life easier - until the technical debt hits ⛔ It’s so easy to create code these days, but reality is quickly catching up to that dream... GitClear‘s AI Copilot Code Quality report analyzed more than 200 million lines of code showing that: 📉 All those code gen quick wins have resulted in a clear decline in code quality: ❗10x more code duplication compared to two years ago ❗ Much less code reuse than ever before (=more redundant systems) So many long-term consequences that people are not thinking about: ❌ Reduced delivery stability ❌ Dramatic escalation in technical debt ❌ Long term maintenance burden, especially for long lived repos ❌ More time developers waste on debugging code and resolving vulnerabilities ❌ Defect remediation and refactoring may soon dominate developer workloads ❌ Financial implications such as more cloud costs, more bugs, more resources... 🎯 Development teams need to put in place some checks and balances. From the article: “Testing becomes a logistical nightmare, heightening the developer’s operational overhead.” One immediate solution is to use AI agents, which provide code testing at scale, alongside your AI code assistants. So go check out Early!

Honored to see Early entering Insight Partners prestigious list of Developer Platforms for testing tools as we step into the era of AI Agents for testing! When you hear the word "AI Agent," ask yourself: ➡️ Which human task is this agent taking off my plate? And what is the quality of its output? ➡️ Is it truly autonomous, or does it still require significant intervention? (Assistant vs. Agent) ➡️ Does it provide enough visibility and control into the process? ➡️ How does it free me up to focus on higher-level, more complex work? And if it’s a task you hate doing (like testing), even better. That’s where AI Agents will truly shine. But here’s another the key insight: Even though AI Agents are the future, trust needs to be built. A fully autonomous agent might be capable of doing 90%-100% of the work, but developers still need a great experience to build confidence in its results. Would you step into a car with no steering wheel or brakes today? Or would you rather see it drive safely on its own for another billion miles first—while still having some control along the way? Excited for what’s ahead!

Test-Driven Development works—so why isn’t everyone doing it? Here’s how AI is making TDD a breeze. Test-Driven Development (TDD) ensures high-quality code by having developers write unit tests before implementing functionality. It encourages developers to think critically about software requirements, leading to cleaner, more modular code. The TDD cycle: 1️⃣ Write a test for a specific feature 2️⃣ Run the test (which initially fails) 3️⃣ Write the minimum code necessary to pass the test 4️⃣ Refactor the code to optimize performance 5️⃣️ Repeat the process until the software is complete The benefits of TDD include: ✅ Improved code quality ✅  Early bug detection ✅ Enhanced refactoring confidence ✅ Better design practices ❓But if it works so well, why do fewer than 5% of developers consistently adopt it? ❗Mostly because it requires significant manual effort, making it tedious and time-consuming. 💭 Wouldn't it be nice if you could streamline the TDD process, eliminate its common pain points, and even enjoy the process? This is where EarlyAI comes in: ⭐ Automated test creation ⭐ Edge-case coverage ⭐ Time efficiency ⭐ Accessibility Learn more about using AI to simplify and accelerate TDD by reading our full blog post ( 👉 link in first comment). It includes a live step-by-step example that walks you through the entire process!

🥱 Tired of wasting time on the boring process of writing tests? What if you could get a full test suite just by describing your expectations in a comment? 💥 ⭐ Lorenzo Zarantonello recently wrote an article on how to boost TDD (Test-Driven Development) using EarlyAI—turning comments into unit tests in one click! He put it perfectly: “Many claim big benefits once you get used to it. But most developers never even bother trying TDD. Most say it’s too time-consuming and boring. So here’s a challenge: use AI to write tests for you—before you start coding.” 💡 Lorenzo used EarlyAI to generate unit tests for a new component simply by adding comments. A few seconds later, he had a fully functional test suite, ready to go. As he summed it up: "EarlyAI is like Bolt.new for testing!" ⚡ 🙏 Huge thanks to Lorenzo for sharing his experience! 👉 Check out the full article for step-by-step examples (link in the first comment).

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