Unit testing is one of the most fundamental practices in software development. It ensures that individual units or components of your code work as expected, preventing bugs and issues from creeping into your applications. However, not all unit tests are created equal. Some are incredibly valuable, while others might be a waste of time.
In this blog, we’ll explore the difference between good and bad unit tests. By the end, you’ll know how to focus on writing tests that truly benefit your codebase and avoid the ones that only slow you down.
What is a Unit Test?
Before diving into the good vs bad debate, let’s quickly revisit what a unit test is.
A unit test is a small piece of code that tests a single function or method in your program to ensure it behaves correctly under various conditions. These tests are usually automated and run each time you make changes to the codebase to catch issues early. It focuses on individual units of work instead of whole applications.
Why are Unit Tests Important?
Unit tests help developers catch bugs early in the development process, improve code reliability, and make refactoring easier. A robust set of unit tests can also act as documentation, demonstrating how your code should behave.
But, here’s the catch – not all unit tests are equally valuable. So, let’s break down what makes a unit test good or not. Each type of test serves a unique purpose and together, they provide a comprehensive safety net for your application.
Good Unit Tests: The Ones You Need
1. Tests with Clear Intentions
The most useful unit tests are the ones that focus on a specific behavior or edge case of your code. Each test should be clear about what it’s trying to validate. If the test doesn’t specify what’s being checked, it’s likely not useful.
// Simple function that sorts a slice of integers
func SortNumbers(nums []int) []int {
for i := 0; i < len(nums)-1; i++ {
for j := i + 1; j < len(nums); j++ {
if nums[i] > nums[j] {
nums[i], nums[j] = nums[j], nums[i]
}
}
}
return nums
}
// Unit test for SortNumbers
func TestSortNumbers(t *testing.T) {
nums := []int{5, 2, 9, 1}
sorted := SortNumbers(nums)
// Check if the list is sorted correctly
expected := []int{1, 2, 5, 9}
for i, v := range sorted {
if v != expected[i] {
t.Errorf("Test failed at index %d: expected %d, got %d", i, expected[i], v)
}
}
}In this example, the unit test has a clear intention: checking if the sorting function works as expected.
2. Tests That Are Small and Focused
Unit tests should be small and focused on testing one thing at a time. A single test should test one unit of code or one behavior. If your test is trying to verify multiple things, it’s probably testing too much, which makes it harder to pinpoint errors when they arise.
// Function that sorts a slice, handling an empty slice
func SortNumbers(nums []int) []int {
if len(nums) == 0 {
return nums // Return empty list as is
}
for i := 0; i < len(nums)-1; i++ {
for j := i + 1; j < len(nums); j++ {
if nums[i] > nums[j] {
nums[i], nums[j] = nums[j], nums[i]
}
}
}
return nums
}
// Unit test for handling an empty slice
func TestSortEmptyList(t *testing.T) {
nums := []int{}
sorted := SortNumbers(nums)
// Check if the result is still an empty list
if len(sorted) != 0 {
t.Errorf("Expected empty list, got %v", sorted)
}
}For example, the test above is verifying both the behavior of a sorting algorithm and whether it handles null values properly, it’s becoming more of an integration test rather than a unit test.
3. Tests That Are Fast
Fast-running tests are the most useful. Unit tests should be quick to execute so that they can be run frequently as part of your development process. If a test takes too long, it may discourage developers from running it, and it might not be as effective.
4. Tests That Provide Valuable Feedback
When unit tests fail, they should provide enough feedback to pinpoint what’s wrong with your code. A useful test will tell you exactly what failed and why, rather than just stating that something broke.
For instance, a helpful error message might tell you that a certain function expects a string but received an integer, guiding you directly to the issue.
Bad Unit Tests: The Ones to Avoid
1. Tests with Too Much Setup
Unit tests should be simple to set up. If your test requires complex dependencies or complicated configuration, it may not be as good as you think. Tests with extensive setup can slow down your development process and create maintenance headaches in the long run.
import (
"testing"
"database/sql"
_ "github.com/lib/pq"
)
// Function that sorts numbers (but unnecessary DB setup is involved)
func SortNumbers(nums []int) []int {
// Sorting logic...
return nums
}
// Unit test with unnecessary DB setup
func TestSortNumbersWithDatabase(t *testing.T) {
db, err := sql.Open("postgres", "user=username dbname=test sslmode=disable")
if err != nil {
t.Fatal(err)
}
defer db.Close()
rows, err := db.Query("SELECT number FROM numbers ORDER BY number")
if err != nil {
t.Fatal(err)
}
var nums []int
for rows.Next() {
var num int
if err := rows.Scan(&num); err != nil {
t.Fatal(err)
}
nums = append(nums, num)
}
// Test sorting using database values
sorted := SortNumbers(nums)
// Check sorting logic...
}This test is bad because it requires unnecessary database interaction for a simple sorting function. Unit tests should avoid such complex setups and focus on testing the function itself.
2. Tests That Are Too Broad
As mentioned earlier, unit tests should focus on a single behavior or unit of code. If your test tries to verify multiple components at once, it can become hard to maintain and debug. Broad tests make it difficult to isolate failures, which undermines the purpose of unit testing.
3. Tests That Are Not Relevant
If your test checks something that doesn’t affect your code’s functionality or doesn’t add value to your system, then it’s bad test. Tests should focus on what’s important for the behavior of the software.
For example, testing how many lines of code a function contains is not going to provide any real value. Stick to testing functionality.
4. Tests That Are Overly Complex
Complex tests may look impressive at first, but they can be a nightmare to maintain. If your test involves too many conditions or hard-to-understand logic, it’s bad as it seems. Keep your tests as simple and straightforward as possible.
// Complex function with multiple behaviors
func SortNumbers(nums []int) []int {
// Sorting logic here
return nums
}
// Overly complex test: Testing too many scenarios at once
func TestSortNumbersComplex(t *testing.T) {
nums1 := []int{5, 2, 9, 1}
nums2 := []int{1}
nums3 := []int{}
nums4 := []int{3, 3, 3}
// Sorting for multiple cases in one test
sorted1 := SortNumbers(nums1)
sorted2 := SortNumbers(nums2)
sorted3 := SortNumbers(nums3)
sorted4 := SortNumbers(nums4)
// Assert all the cases here in one test
if sorted1[0] != 1 || sorted1[1] != 2 || sorted1[2] != 5 || sorted1[3] != 9 {
t.Errorf("Failed on first case: %v", sorted1)
}
if sorted2[0] != 1 {
t.Errorf("Failed on second case: %v", sorted2)
}
if len(sorted3) != 0 {
t.Errorf("Failed on third case: %v", sorted3)
}
if sorted4[0] != 3 || sorted4[1] != 3 || sorted4[2] != 3 {
t.Errorf("Failed on fourth case: %v", sorted4)
}
}This test is overly complex because it’s verifying multiple conditions at once. Each test should focus on one thing at a time, which makes it easier to identify the root cause of a failure.
How to Write Good Unit Tests?
Writing unit tests is all about focusing on the essential parts of your code. Here are some tips for writing tests that will actually help you:
-
Keep It Small and Isolated
Make sure each test focuses on one small part of your application. Isolate each unit and test it in isolation to avoid dependencies on other parts of the system.
-
Write Tests That Handle Edge Cases
Don’t just test the “happy path.” Consider all possible edge cases. For example, test what happens when inputs are null, empty, or contain unexpected values.
-
Run Tests Often
Run your unit tests often – after every change if possible. This helps catch errors early and makes sure your code stays functional.
-
Refactor Tests When Necessary
As your codebase evolves, some tests may become irrelevant or redundant. Refactor your tests just like you refactor your code.
Generating Good Unit test with AI
With AI, the testing process becomes smarter, more adaptive, and significantly faster. Instead of wasting time writing and maintaining tests that aren’t really adding value, developers can rely on AI to generate high-quality unit tests that focus on testing core functionalities and edge cases. This reduces the overhead of manual test creation while ensuring that tests remain precise, relevant, and effective. But it comes with it’s own set of challenges!
Tools like ChatGPT and GitHub Copilot can speed up unit test generation but face challenges such as limited context understanding, lack of domain-specific knowledge, and difficulties in handling complex or evolving code, requiring developers to validate and fine-tune tests for accuracy and relevance.
Keploy: The AI-based Unit Test Generator
Keploy, is an tool designed to help developers generate useful unit tests automatically. It uses AI to analyze your code and create tests that are specifically designed to verify the most logical aspects of your application’s functionality.
How Keploy works?
Let’s say you have a function that processes user data. Traditionally, writing unit tests would require you to manually identify edge cases, test for various inputs, and set up each test scenario. With Keploy, you can simply provide your code, and Keploy will automatically generate relevant unit tests like:
-
Validating input data (e.g., testing null values, invalid inputs).
-
Boundary checks (e.g., ensuring data processing works within expected ranges).
-
Behavioral tests (e.g., checking if your function produces correct output given a certain set of conditions).
The Future of Unit Testing with AI
AI-powered testing tools like Keploy, DiffBlue represent the future of unit testing – enabling developers to write cleaner, more effective tests while focusing on the core functionality of their applications.
By leveraging AI, these tools ensures that you can continuously improve your codebase without the burden of maintaining a cumbersome and inefficient testing process. No more useless tests clogging up your workflow – just precise, valuable unit tests that help you build better software, faster.
Conclusion
Unit testing is essential for building reliable and maintainable software. However, not all unit tests are created equal. Understanding the difference between good and bad tests is crucial for ensuring your testing process helps you rather than hinders you.
Focus on writing small, isolated tests that cover important cases and provide quick, actionable feedback. And remember, a unit test should never feel like a chore – it should enhance your workflow, not slow you down! So, next time you write a unit test, ask yourself: Is this test truly good way to write?
FAQ
Why are unit tests so important?
Unit tests help catch bugs early, provide documentation for your code, and improve the overall reliability of your application. They also make refactoring easier and more confident.
Can I skip writing unit tests for my project?
While it’s tempting to skip unit testing for faster development, it can lead to long-term maintenance issues. Unit tests save time in the long run by preventing bugs and reducing the amount of manual testing required.
How do I know if my unit test is useful?
A useful unit test is clear, focused, fast, and provides valuable feedback when it fails. It should test one small part of your application, handle edge cases, and be easy to maintain.
Should I always write unit tests before coding?
Writing tests before coding is part of the TDD (Test-Driven Development) approach. While it’s not required, it’s an excellent practice for ensuring that your code meets specific requirements and behaves as expected.
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