AI-Assisted Drupal Module Development Shows Tests and Specifications Drive Outcomes

A three-part series by Niels de Feyter, founder of CodeLift, examines how a Drupal contributed module developed through multiple AI-assisted iterations reached production readiness through specifications and testing rather than tooling. The final instalment, published on 19 March 2026, focuses on the development method behind the module config_uuid_deterministic.

The account describes a 14-month process in which the module was rewritten three times using different AI coding tools, with each version required to pass the same PHPUnit-based test suite and run in production. The consistency of outcomes across iterations is presented as evidence that requirements and validation criteria—not the tools—determined the final implementation.

The development approach began with a detailed functional specification created before any code generation. The specification defined deterministic UUID behaviour, configuration handling rules, integration with Drupal tools, and test coverage requirements. This document remained stable across later iterations, serving as the contract that all implementations had to satisfy.

Test-driven development structured the workflow. The final iteration started with failing tests covering all specified behaviours, followed by repeated cycles of implementation and validation. The resulting version achieved over 95% test coverage and introduced continuous integration across multiple PHP and Drupal versions, marking a transition from prototype to publishable contributed module.

Production use played a critical role in shaping the test suite. A failure involving hashed database table names revealed a scenario not anticipated in the original specification, leading to additional tests and iterative fixes. The case illustrates that real-world deployment exposed constraints that neither the specification nor AI-generated code had predicted.

The series also distinguishes between areas where AI tools were effective and where they required intervention. Tasks such as generating test scaffolding, implementing clearly defined algorithms, and refactoring code were handled efficiently. However, the tools consistently struggled with Drupal’s configuration lifecycle, service architecture, and edge cases involving data persistence and schema behaviour.

The author concludes that AI tools function as interchangeable execution layers within a development process defined by human decisions. Writing specifications, designing tests, and making architectural trade-offs remained outside the scope of automation. The methodology—specification first, followed by test-driven iteration—emerges as the primary factor in achieving a stable and portable module across environments.