Spec-driven code generation is the process of using a detailed specification, often written in natural language, to automatically generate source code, unit tests, and other development artifacts. Instead of manually translating requirements into code, developers create a precise description of the feature or function, which a code generation model then uses as a blueprint. This approach aims to accelerate development, reduce human error, and ensure the final code directly maps to the initial requirements.

The “spec” can range from a simple function signature with a docstring to a comprehensive document outlining business logic, data models, and API endpoints. The goal is to create a system where a natural-language request can be programmatically translated into a fully-formed, tested, and ready-to-merge pull request (PR).

The process transforms a natural-language specification into a pull request through a series of automated, measurable steps. This workflow ensures that the generated code is not just functional but also reliable, secure, and aligned with project standards.

1. Specification to Test Generation: The process begins by feeding a detailed, natural-language specification of a feature into a language model. The model’s first task is to interpret the requirements and generate a corresponding set of unit tests. These tests act as a concrete, evaluable contract that the final code must fulfill.
2. Model Bake-Off: With a clear set of tests, one or more code generation models are tasked with writing the source code to satisfy them. This “bake-off” pits different models or differently-prompted models against each other. Each model’s output is run against the generated test suite.
3. Automated Evaluation: The generated code is subjected to a gauntlet of automated checks to measure its quality and readiness. This evaluation is multi-faceted and goes far beyond simple test-passing.
4. Pull Request Creation: The winning code—the one that performs best across all evaluation criteria—is packaged into a pull request, complete with the generated code, tests, and a summary of the evaluation metrics.

This structured approach turns code generation from a creative exercise into an engineering discipline, where outputs are consistently measured and improved.

Making code generation measurable is crucial for moving beyond novelty and into production-grade engineering. Without objective evaluation, it’s impossible to trust, maintain, or scale AI-driven development. Measurability provides the confidence needed to integrate generated code into a professional software development lifecycle.

Key benefits of a measurable process include:

• Trust and Reliability: Automated checks and balances ensure that generated code meets quality standards before a human ever reviews it.
• Objective Comparisons: When running a “bake-off,” objective metrics allow you to select the best-performing model for the task, rather than relying on subjective judgment.
• Continuous Improvement: By tracking metrics over time, you can identify weaknesses in your prompts, models, or evaluation criteria, allowing for continuous refinement of the entire process.
• Scalability: A measurable, automated pipeline allows teams to scale their development efforts without a linear increase in human oversight.

Evaluating generated code requires a multi-layered approach that assesses correctness, quality, and security. Relying on a single metric, like test passage, is insufficient. A robust evaluation pipeline incorporates several types of checks.

Compile and Lint Checks This is the first and most basic gate. Does the code even compile? Does it adhere to the project’s established style guides and linting rules? Failing at this stage indicates a fundamental problem with the generated output.

Unit Test and Coverage Deltas The core of the evaluation process is running the generated code against the spec-derived unit tests. Key metrics include:

• Test Pass Rate: The percentage of tests that pass.
• Test Coverage Delta: How much did the new code increase (or decrease) the overall test coverage of the codebase? A positive delta is a strong signal of quality.

Abstract Syntax Tree (AST) Diffs An AST represents the code’s structure. Analyzing the AST allows for more sophisticated checks than simple text-based diffs. For example, you can enforce rules like “no new public methods should be added to this critical class” or “database calls are only allowed in the data access layer.”

Security Scanners Static Application Security Testing (SAST) tools can be integrated into the pipeline to scan the generated code for common vulnerabilities, such as SQL injection, cross-site scripting (XSS), or insecure direct object references. This ensures that the generated code doesn’t introduce new security risks.

A Sample Evaluation Matrix

This table provides a simple framework for scoring the output from different models in a bake-off, allowing for a data-driven decision on which code to advance to a PR.

While powerful, building an automated spec-to-PR pipeline comes with its own set of challenges. These are not insurmountable but require careful consideration and engineering effort.

• Crafting Good Specs: The quality of the output is highly dependent on the quality of the input. Vague, ambiguous, or incomplete specifications will lead to poor-quality tests and code. Teams need to develop a skill for writing clear, machine-readable specs.
• Environment and Dependency Management: The evaluation pipeline needs to be able to reliably build and test the generated code. This requires a containerized, reproducible environment with all necessary dependencies, which can be complex to set up and maintain.
• Cost of Computation: Running multiple models and a full suite of evaluation tools can be computationally expensive. This is especially true for large codebases or complex specifications. Optimizing the pipeline for efficiency is key.
• Handling “Wrong” but Passing Code: It’s possible for generated code to pass all the tests but still be incorrect from a business logic perspective. The generated tests might not cover all edge cases. This is why human review of the final PR remains a critical step.

Integrating generated code into a real-world application often involves interacting with external services for authentication, authorization, and feature management. This is where a service like Kinde, with its robust APIs and SDKs, can simplify the process.

When your natural-language spec includes requirements like “this endpoint should only be accessible by administrators” or “this new dashboard feature should be hidden behind a feature flag,” the code generation model can be prompted to use Kinde’s tools.

For instance, the model could generate code that uses a Kinde SDK to:

• Check for permissions: Before executing a critical function, the code would verify the user’s permissions.
• Toggle features with flags: The visibility of a new UI component could be wrapped in a call to check a Kinde feature flag. You can learn more about the different types of feature flags Kinde offers.

Because these interactions can be managed programmatically, they fit perfectly into a spec-to-PR pipeline. Your evaluation step can even include checks to ensure that the generated code correctly implements these calls. For more advanced use cases, you can manage your feature flags through the Kinde Management API, allowing your pipeline to create and toggle flags as part of the code generation process itself.

• About feature flags
• Manage feature flags through the Kinde API