Debugging & Test Case Generation
Leverage LLMs for identifying bugs, suggesting fixes, and automatically generating comprehensive test cases for your software.
Debugging & Test Case Generation is a free Prompt Engineering & LLM Optimization for Developers lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Prompt Engineering & LLM Optimization for Developers learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
LLMs: Your Debugging Assistant
Debugging can be a time-consuming part of software development. Large Language Models (LLMs) can act as powerful assistants, helping you identify issues faster and suggesting solutions.
They analyze your code, understand its intent, and spot common pitfalls that might otherwise take hours to find.
Identifying Syntax & Logic Bugs
LLMs are adept at detecting various types of errors in your code:
- Syntax Errors: Missing semicolons, mismatched parentheses, incorrect keywords.
- Logical Flaws: Subtle mistakes in your algorithm that cause incorrect behavior.
- Potential Issues: Suggesting areas for improvement or common anti-patterns.
Example: Spotting a Syntax Error
Consider this simple Java code. An LLM can quickly point out the missing semicolon. Try running it to see the error!
public class BuggyCode {
public static void main(String[] args) {
int x = 10
System.out.println("Value: " + x);
}
}Suggesting Code Fixes
Beyond just identifying errors, LLMs can also propose concrete fixes. This is incredibly useful as it not only highlights the problem but also provides a path to resolution.
You can prompt an LLM with your buggy code and ask it to 'fix this code' or 'explain the error and provide a solution'.
Example: Fixing a Logical Flaw
This Java function intends to return the maximum of two numbers, but it has a logical bug. An LLM could suggest changing the `if` condition to `a > b`.
public class MaxFinder {
public static int findMax(int a, int b) {
if (a < b) { // Logical error here!
return a;
} else {
return b;
}
}
public static void main(String[] args) {
System.out.println("Max of 5 and 10: " + findMax(5, 10)); // Should be 10, outputs 5
System.out.println("Max of 20 and 7: " + findMax(20, 7)); // Outputs 7
}
}Automating Test Case Generation
Writing comprehensive unit tests is crucial for software quality, but it can be repetitive. LLMs can automate this by generating test cases for your functions and methods.
This speeds up development and ensures better code coverage.
Prompting for Test Cases
To get the best test cases from an LLM, your prompt should include:
- The code snippet or function signature.
- The desired testing framework (e.g., JUnit for Java, Pytest for Python).
- Specific scenarios (e.g., positive, negative, edge cases).
- The desired output format (e.g., 'only the test code').
Example: Generating Unit Tests
Given a simple function, an LLM can generate a JUnit test class. Here's a sample function and the tests an LLM might produce for it.
public class Calculator {
public int add(int a, int b) {
return a + b;
}
}
// --- LLM Generated Tests ---
import org.junit.jupiter.api.Test;
import static org.junit.jupiter.api.Assertions.assertEquals;
public class CalculatorTest {
@Test
void testAddPositiveNumbers() {
Calculator calc = new Calculator();
assertEquals(5, calc.add(2, 3));
}
@Test
void testAddNegativeNumbers() {
Calculator calc = new Calculator();
assertEquals(-5, calc.add(-2, -3));
}
@Test
void testAddZero() {
Calculator calc = new Calculator();
assertEquals(10, calc.add(10, 0));
}
}Covering Edge Cases with LLMs
LLMs are excellent at thinking through various scenarios, including crucial 'edge cases' that developers sometimes overlook.
Prompt them to generate tests for:
- Empty inputs (e.g., empty strings, null lists)
- Zero, maximum, or minimum values
- Invalid inputs (e.g., non-numeric where numbers are expected)
- Duplicate data
Debugging & Testing Quiz
Test your understanding of how LLMs assist in debugging and test case generation.
Recap: Debugging & Testing with LLMs
In this lesson, you learned how LLMs can be powerful allies in your development workflow:
- They can identify and suggest fixes for both syntax and logical errors.
- LLMs are valuable tools for automating the generation of comprehensive unit test cases.
- By crafting effective prompts, you can guide LLMs to cover positive, negative, and critical edge case scenarios, significantly enhancing code quality and robustness.
Frequently asked questions
Is the “Debugging & Test Case Generation” lesson free?
Yes — the full text of “Debugging & Test Case Generation” is free to read here on the web, and the Prompt Engineering & LLM Optimization for Developers course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Prompt Engineering & LLM Optimization for Developers course, upgrade to CoddyKit PRO.
What will I learn in “Debugging & Test Case Generation”?
Leverage LLMs for identifying bugs, suggesting fixes, and automatically generating comprehensive test cases for your software. You practise Prompt Engineering & LLM Optimization for Developers with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Prompt Engineering & LLM Optimization for Developers?
No prior experience is required. Prompt Engineering & LLM Optimization for Developers on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Debugging & Test Case Generation” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Prompt Engineering & LLM Optimization for Developers lesson?
Yes. Every Prompt Engineering & LLM Optimization for Developers lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Code Generation & Refactoring
- Debugging & Test Case Generation
- Data Extraction & Summarization
- Generating SQL Queries from Natural Language