TDD vs BDD with Amazon Q Developer
Why Test-Driven Development Shines with Code Assistants
Published May 11, 2025
The Context Advantage: A Developer's Story
The Initial Approach: BDD with Amazon Q
The Turning Point: Discovering TDD's Power
Why TDD Works Better with LLM-Based Tools
1. Concrete Implementation Details
3. Precision in Error Handling
The Hybrid Approach: Getting the Best of Both Worlds
Conclusion: TDD as the Secret Weapon for LLM-Powered Development
As I sat down to build the Simple Budget Tracker application with Amazon Q Developer, I faced a critical decision at the outset: should I follow Behavior-Driven Development (BDD) or Test-Driven Development (TDD)? While both methodologies have their merits, my experience revealed that TDD offers distinct advantages when working with Large Language Model (LLM) based coding assistants like Amazon Q.
You can refer the Blog 1: Building a Full-Stack Budget Tracker Application with Amazon Q CLI: An Iterative Approach
You can refer the Blog 2: Deploying a Flask Application to AWS Fargate with Amazon Q: A Journey of "No-Touch Coding"
This approach was excellent for defining user-centric requirements. Amazon Q understood the high-level functionality I wanted to build. However, when it came to generating the actual implementation, something was missing.
When I switched to TDD, providing Amazon Q with test cases instead, something remarkable happened:
The code Amazon Q generated was significantly more precise, aligned perfectly with my needs, and required fewer iterations to get right.
BDD focuses on behavior from a user's perspective, which is valuable but abstract. TDD, on the other hand, provides concrete implementation details - method names, parameters, return types, and assertions. For our Budget Tracker application, the test cases clearly defined the
BudgetTracker class structure, the add_expense method signature, and the expected behavior of the get_balance method.This level of detail gives Amazon Q's underlying LLM much more context about what you're trying to build. It's like giving precise measurements to a carpenter instead of just describing the furniture you want.
While BDD provides natural language specifications, TDD provides executable specifications. When I provided Amazon Q with test cases for filtering expenses by date range:
Amazon Q immediately understood not just that I wanted date filtering, but exactly how the filtering should work, what edge cases to handle, and how to structure the method.
TDD excels at defining how your code should handle edge cases and errors. When developing the Budget Tracker's validation logic, I included tests for invalid inputs:
This simple test case gave Amazon Q crucial information about how to handle validation, resulting in robust error handling in the generated code:
When building the Flask API layer of our Budget Tracker, TDD provided a clear path for iterative development. Each test defined a specific endpoint and its expected behavior:
Amazon Q used these tests to generate not just the endpoint implementation, but also proper request validation, error handling, and response formatting - all aligned with the test expectations.
While TDD proved more effective with Amazon Q, I didn't abandon BDD entirely. Instead, I adopted a hybrid approach:
- Start with BDD to define high-level requirements and user stories
- Translate these into TDD test cases to provide detailed implementation guidance
- Use Amazon Q to generate code based on the test cases
- Iterate on the tests and implementation as needed
This approach gave Amazon Q both the "what" (from BDD) and the "how" (from TDD), resulting in higher quality code with fewer iterations.
When working with LLM-based coding assistants like Amazon Q Developer, Test-Driven Development provides the rich, detailed context that these models need to generate precise, high-quality code. While BDD helps define what your application should do from a user's perspective, TDD tells the AI exactly how it should be implemented.
In our Budget Tracker project, this approach led to:
- Fewer iterations to get the code right
- More precise implementation of features
- Better handling of edge cases and errors
- A clearer development path from core functionality to API endpoints
For developers looking to maximize the effectiveness of AI coding assistants, starting with well-defined test cases might be the most powerful approach you can take. The tests not only guide the AI but also serve as documentation and validation for the generated code - a win-win for development efficiency and code quality.
As LLM-based development tools continue to evolve, the developers who can provide the most precise context through methodologies like TDD will be the ones who harness their full potential.