Remember back then, every time the boss asked "how much % of coverage is there?" My heart "dropped" a beat again. Writing unit tests is both boring and time-consuming, and the deadline is always close. Painstakingly typing each line of mock data or setting up the environment is truly a nightmare for any programmer during the software development process.
But recently, I have found "true love" that helps automate nearly 80% of this boring job. Thanks to the AI application to write automatic unit tests for the project, specifically through the hands of "assistants" like Copilot or Gemini, everything has completely changed. At Pham Hai, we have applied this process and saved countless hours of stressful work. In this article, I will share all the ways I get AI tools to create unit tests quickly, accurately, and most effectively up to the present time.
Get AI to 'write tests' for you: A detailed guide with Copilot and Gemini
Using AI to write unit tests now only requires a few simple prompts or mouse clicks right in the IDE, helping to automate the creation of test cases and mock data extremely quickly.
If you're looking for the smoothest way for AI to write unit tests, the secret lies in choosing the right tools and communicating with them properly. Below are practical instructions that I have concluded after hundreds of hours of "struggling" with code.
With GitHub Copilot: Your assistant right in VS Code
GitHub Copilot cho phép bạn dùng lệnh @Test trong chat hoặc click chuột phải để tạo ngay các file unit test chuẩn xác dựa trên ngữ cảnh code hiện tại.
Vào tháng 2 năm 2026, bản cập nhật Visual Studio 2026 v18.3 đã mang đến một bước tiến khổng lồ cho Copilot. Microsoft đã tích hợp một "purpose-built AI test agent" (đặc vụ AI chuyên trách kiểm thử) hiểu sâu sắc cấu trúc project của bạn. Copilot viết unit test như thế nào? Rất đơn giản, bạn chỉ cần mở Copilot Chat, gõ @Test generate unit tests for [tên class].
It not only generates code but also automatically builds and tests using frameworks like NUnit, xUnit or MSTest, and automatically fixes errors if the test fails. If you are wondering whether to invest in a paid account for this tool, the review article Is GitHub Copilot worth using for developers will give you the most objective perspective.
| Implementation step | Working with Copilot | Expected results |
|---|---|---|
| Bước 1 | Highlight the C# or Python function/class that needs to be tested | Copilot gets the context of the code |
| Bước 2 | Gõ /tests hoặc @Test trong khung Chat |
AI analyzes logic and edge cases |
| Bước 3 | Press "Accept" or "Run" directly | File unittest hoặc pytest được tạo và chạy |
With Google Gemini: The power of large language models
Gemini, especially through the latest Gemini CLI of 2026, can analyze the entire project with a huge context window to write tests that cover complex edge cases.
Gemini viết unit test ra sao khi dự án của bạn có hàng trăm file liên kết chằng chịt? Điểm mạnh nhất của Google Gemini trong năm 2026 chính là khả năng đọc hiểu ngữ cảnh lên tới 1 triệu token. Bạn có thể sử dụng Gemini CLI ngay trong terminal với câu lệnh: gemini "Generate pytest tests for every function in the /api folder".
This large language model (LLM) is especially good at finding logic flaws. I often use the "Context-First" strategy to request it: "Play the role of a demanding QA engineer, find 5 edge cases that can crash this function and write tests for them using JUnit". To better understand the capabilities of current AI models, you can refer to the article Comparing ChatGPT vs Claude vs Gemini that I compiled.
Bonus: Use JetBrains' AI Assistant (IntelliJ, PyCharm)
JetBrains AI Assistant tích hợp sâu vào IDE, hỗ trợ tạo unit test thông minh bằng phím tắt Alt+Enter và tự động nhận diện framework phù hợp.
Nếu bạn là fan cứng của hệ sinh thái JetBrains (code Java, Python, JavaScript), thì JetBrains AI Assistant (được trợ lực bởi mô hình Mellum) là một lựa chọn tuyệt vời. Chỉ cần đặt con trỏ chuột vào tên hàm, nhấn Alt+Enter và chọn "Generate test content with AI". Công cụ này cực kỳ hiểu cấu trúc AST (Abstract Syntax Tree) của code.
Of course, on the market there are many other excellent IDEs that integrate AI. If you like a completely new coding experience and optimized for AI from the core, check out Cursor AI editor code with guided AI for more options in your toolkit. For projects that require absolute source code security, setting up a local LLM running AI on a personal computer to generate offline test cases is also a rising trend this year.
Not just 'writing tests for you': Real benefits AI brings to projects
AI not only types code for you, but also helps speed up releases, improve test coverage, and detect potential errors early to minimize repair costs.
Applying AI to write automatic unit tests for projects brings value beyond just saving a few keystrokes. AI benefits in software testing are reshaping the entire QA process of technology businesses.
Increase development speed, reduce 'waiting' time
By automating boring boilerplate code, AI helps programmers complete unit testing tasks in minutes instead of hours.
In the past, applying TDD (Test-Driven Development) or BDD (Behavior-Driven Development) was often criticized for slowing down project progress. Now, AI can generate test case frameworks instantly. Theo thống kê từ sự kiện Transform 2025 của Tricentis, việc dùng AI agents đã giúp một số đội ngũ giảm 85% nỗ lực thủ công và tăng 60% năng suất. This speed helps the dev team push code to the staging environment much faster.
Increase test coverage up to 80%
AI tools have the ability to scan entire files and automatically create a series of test cases, easily pushing project test coverage to 80% or higher.
Tăng độ bao phủ kiểm thử bằng AI không còn là lời hứa suông. Thay vì chỉ viết vài test case "happy path" cho có lệ, AI sẽ tự động sinh ra các kịch bản kiểm thử cho các nhánh if/else ẩn sâu bên trong. Việc duy trì độ bao phủ kiểm thử ở mức cao giúp đảm bảo chất lượng mã nguồn luôn ổn định mỗi khi có thay đổi mới được merge vào nhánh chính.
Detect bugs earlier, reducing error correction costs
Writing more and faster tests means that logic bugs are discovered right from the development stage, helping to significantly save on the cost of fixing bugs in production.
The later an error is detected, the higher the cost of correction. When AI creates test scenarios that cover edge cases, vulnerabilities will be exposed immediately during the CI/CD integration process. Sometimes, the process of AI writing tests reveals code smells that need to be refactored. If you have difficulty detecting the root cause of errors, the AI debug code to find errors faster tool will be a great assistant that comes with a solid unit testing system.
Frees programmers from repetitive tasks
Programmers no longer have to waste time writing mock data or setting up manual test environments, thereby focusing on solving core business logic problems.
Creating mock objects (mocks) for databases or external APIs is often very tedious and boring. AI solves this in the blink of an eye. Thanks to unit test automation using AI, programmers and QA engineers can spend their valuable time thinking about system architecture or designing more complex integration testing strategies. Working efficiency has since improved significantly.
AI is not a 'magic wand': Challenges and facts to know
Although powerful, AI still has limitations such as the ability to create false test cases, fabricate information, and difficulty handling very specific operations.
At Pham Hai, we always remind the team that: AI is an assistant, not a boss. The challenge of AI in unit testing still exists and if you close your eyes and trust 100%, the consequences will be very unpredictable.
AI can 'hallucinate' and create false test cases
Sometimes AI deduces functions that do not exist or writes test passes but does not actually check the correct logic, requiring high caution.
Một ví dụ AI tạo unit test kinh điển là nó tự động "bịa" ra một phương thức getUserById() trong class Repository dù bạn chưa hề viết nó. Hiện tượng này rất phổ biến khi AI cố gắng làm hài lòng người dùng bằng mọi giá. Để hiểu rõ cơ chế tại sao LLM lại hay "nói dối" như vậy, bạn nên đọc qua bài phân tích AI hallucination vấn đề AI bịa thông tin. Đừng bao giờ thấy test báo màu xanh (pass) là vội mừng!
Risk of dependence and reducing the programmer's logical thinking
Overuse of AI can make junior developers lazy in thinking, losing the ability to design sharp test scenarios themselves.
If you click the "Generate Tests" button, new programmers will lose the opportunity to practice critical thinking. Writing a difficult test case yourself helps you deeply understand how the system operates. Overuse of AI over time will lead to the situation of a "code typist" instead of a real software engineer.
Humans still need to review and refine the results
Code generated by AI always needs to be reviewed by an experienced engineer to ensure accuracy and compliance with the project's coding convention.
The code review process is still the final safety barrier. Sometimes AI writes very lengthy tests or uses outdated libraries. For AI to generate quality code from the start, you need to know how to give it commands. The Prompt Engineering skill of writing standard prompts for AI is currently a required survival skill to control these models in the right direction, instead of letting them "run free".
Difficulty with complex business logic
AI works well with standalone utility functions, but is often "confused" by cross-functional business flows that require deep domain knowledge.
For example, when you need to write tests for a shopping cart checkout flow in Laravel or FastAPI that involves calculating taxes, applying discount codes, and calling third-party banking APIs. AI can write the basic structure, but it cannot "smell" your company's specific business rules. At this time, humans must intervene to fine-tune API testing and integration testing scripts for accuracy.
The future of QA industry: Will AI completely replace Testers?
AI will not take away testers' jobs, but it will be a tool to eliminate those who refuse to change and upgrade their skills.
With the explosion of AI writing automatic unit tests for projects, the biggest question that arises is: Will AI replace testers? According to the 2026 State of Testing report, 76.8% of QA teams have adopted AI to some extent. However, the real picture is not as gray as many people fear.
Short answer: NO
Machines cannot yet possess critical thinking, user empathy and the ability to discover errors (exploratory testing) like a real human.
No matter how smart the AI tools that support programming are, they still lack the "intuition" of a skilled manual tester. AI doesn't know how to pretend to make a user angry, repeatedly entering the wrong format to see if the app crashes or not. It also cannot evaluate the UI/UX experience of whether the button is convenient or not.
AI is a tool, not a problem solver
AI can only execute what it is told. Orientation, strategic design and final decision making are still in the hands of humans.
You can think of AI as a super-fast plow, but the farmer still has to decide which field to plow. If you are new to using these tools, ChatGPT effective usage guide 2026 will provide you with the most basic foundations to start automating daily work, from writing instructions for using ChatGPT to writing unit tests to creating bug reports.
QA's role will shift: From manual execution to monitoring and designing test strategies
QA engineers will transform into "orchestrators" - AI coordinators, focusing on risk management and designing high-level test scenarios.
Instead of manually clicking through API testing automation streams, QA in 2026 will use time to optimize the QA process with AI. They will analyze data, identify risk areas (risk-based testing) and evaluate the quality of the test cases generated by AI.
In short, AI writing automatic unit tests for projects is no longer a fantasy or a temporary "trend". It has become a mandatory skill to survive in the modern software industry. It helps us work faster, cover more difficult cases, but it certainly cannot completely replace the thinking of an engineer. Our job is to learn how to "command" it effectively, turning it into the most effective tool in our toolkit. Don't be afraid of AI, learn to master it.
Have you used AI to write unit tests for your project? Share a 'trick' or a 'trick' you encountered in the comments section below!
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