How test-driven development amplifies AI success

AI's new reality: how TDD principles amplify your success. The 2025 DORA report finds that AI, while powerful, acts as an amplifier, making existing good practices even more effective. This means that foundational principles like those found in test-driven development (TDD) are more critical than ever. TDD can help you leverage AI to accelerate development, improve code quality, and increase team performance. To learn more about these findings, you can download the full DORA 2025 report.

What is test-driven development?

Test-driven development, or TDD, is a software development approach focused on writing tests before writing the actual code.

It's a method where tests guide the entire development process, helping to create cleaner, more reliable, and better-designed code. It is a design practice that helps you think about what your code needs to do before you write it. For projects where writing code is not the main task, you may want to explore low-code and no-code solutions that can help you implement similar testing principles.

The TDD workflow: Red, green, refactor

The TDD workflow is a cycle that developers repeat over and over again. It is often called the "red, green, refactor" cycle, reflecting the status of your tests.

The first step is to write a test for a new piece of functionality. When you run this test, it should fail, which shows that the test is working correctly, and that the new feature isn’t in the code yet.

With a failing test in place, the next step is to write the minimal amount of code required for it to pass. The sole objective is to pass the test, without concern for code perfection or elegance.

Once the test is passing, you can improve the code. Refactoring is the process of cleaning up your code's design without changing how it works. Since you have a passing test, you can make changes with confidence, knowing you won't break anything.

TDD and the age of AI

The DORA report highlights a complex relationship between AI and software delivery quality. While AI can boost individual productivity and perceived code quality, it can also increase instability if not paired with strong foundational practices.

  • AI for testing: The report reveals that AI is being used for a variety of quality-related tasks, such as generating test cases and debugging. In fact, 62% of developers who write tests use AI to assist them. AI can also continuously analyze code for bugs and security vulnerabilities and dynamically generate new tests.
  • Impact on code quality: AI adoption is associated with an increase in perceived code quality, with 59% of respondents reporting a positive impact. However, the report also notes that AI adoption is still associated with an increase in software delivery instability, which suggests a need for robust control systems, such as strong automated testing, to prevent new issues from being introduced. The report suggests that AI is seen as a productivity accelerator whose output should be funneled into pipelines that are guarded by version control, automated tests, and human review.
  • A "trust but verify" approach: While AI can be helpful, the report notes that a significant portion of developers (30%) still report little to no trust in AI-generated code. This highlights the ongoing importance of human oversight and verification.

The value of TDD principles for AI-assisted development

The DORA report's findings show that the core principles of TDD are more relevant than ever. By focusing on quality and iterative improvement, teams can effectively harness the power of AI while mitigating its risks. The report emphasizes that AI’s benefits are amplified when combined with these practices.

Working in small batches

The report confirms that working in small batches is a critical capability for high-performing teams. This practice allows teams to get feedback on changes quickly and more easily address issues. When AI is used in conjunction with this practice, it has a positive impact on product performance and helps to reduce friction within the development process.

Confidence in changes

Since TDD prioritizes writing tests first, it gives developers a high degree of confidence when making changes because the tests act as a safety net. This is particularly important in an AI-assisted environment where the volume and velocity of changes are dramatically increasing. The DORA report finds that using a version control system's rollback feature more frequently is associated with better team performance.

Systemic approach

The report highlights that successful AI adoption is a systems problem, not a tools problem. A well-designed internal platform provides the necessary guardrails and shared capabilities, ensuring that AI-generated code is safe and secure. These platforms help scale AI benefits from local optimizations to systemic organizational improvements.

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Further reading

Learn more about code quality and automated testing: To dive deeper into these subjects and understand how your team can leverage these capabilities, refer to the DORA report.These sections will provide additional context and insights:

  • The DORA AI Capabilities Model chapter identifies key practices, such as working in small batches, that amplify the positive impacts of AI
  • The Exploring AI's Relationship to Key Outcomes and Platform Engineering chapters provide more detail on the relationship between AI adoption, software delivery stability, and the role of automated quality checks

Learn about working in small batches: The DORA website offers guides on test automation and working in small batches, which include practical steps and metrics to help your team improve.

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