Automated Code Generation and Custom IP Integration: Achieving 70% Boilerplate Coverage with Dynamic Module Loading

In my previous articles, I outlined:

1. How an open-source software delivery model can transform large-scale product development.
2. Why a modular, loosely coupled, and context-driven loadable architecture is the backbone for sustainable, resilient systems.

Now, I am continuing this series by focusing on Generative AI to tackle the often-overlooked (yet resource-heavy) boilerplate code problem. Every engineering organization grapples with repetitive setups—creating similar modules, controllers, configuration files, or integration points for each new service or feature. By harnessing AI-driven code generation, you can accelerate development, freeing your team to focus on high-value, custom IP (Intellectual Property).

Let’s dive into how to build a practical, AI-assisted approach using Java modules, Spring Boot, and Groovy—all while preserving your unique domain knowledge and bridging it seamlessly into your codebases.

?Generative code reuse is not a new concept.? It has been around for as long as creative people wanted to reduce the time needed to bring ideas to completion or market.? The challenge with most approaches has been dealing with custom code that somehow gets intertwined with the generated code leaving most generative reuse a one time shot.? With Generative AI, there are possible solutions to this challenge, but it may require a company to either a) rely completely on internal LLMs or b) leak their core IP out into the public LLMs.? If one were to cleanly separate out the boilerplate from the core IP, it’s conceivable that one could achieve the best of generative reuse while removing the risk of leaking core IP out of their world.

At a high level, some key points to consider include:

1. Generative AI Meets Modular Architecture

Before we get into the practical steps, let’s revisit a core principle: Your architecture must support continuous evolution. As discussed in the second article, a modular, loosely coupled system allows for:

• Easy Replacement of components without widespread system disruption.
• Parallel Development by multiple teams or an “elastic engineering pool.”
• Seamless Integration of new features, whether human-authored or AI-generated.

Generative AI thrives in this context because it can spin up new modules, microservices, or package structures on-demand, with minimal friction.

2. Practical Steps to Building Generative AI Templates

2.1 Identify Common Boilerplate

1. Layer-Specific Setup: Data access layers, REST controllers, authentication modules, etc.
2. Infrastructure-As-Code: Standard Dockerfiles, Kubernetes manifests, or CI/CD pipeline scripts.
3. Configuration and Deployment: Common patterns in application.yml (Spring Boot), module-info.java (Java), or build.gradle (Groovy/Gradle).

By documenting these recurring patterns, you lay the groundwork for AI-generated scaffolding that covers a significant chunk of your code.

2.2 Develop AI-Friendly Blueprints

1. Annotated Templates: Create a well-documented base for each type of component.
2. AI Training Data: If you’re using an internal generative model, feed it examples of best-practice code.? However, for boilerplate code, you should be free to use public LLMs as there is no IP within this code.? This will enable your organization to leverage best practices without compromising internal proprietary solutions.
3. Code Snippets with Clear Boundaries: Keep each snippet minimal and modular—this fosters easy blending with custom IP.

Tip: Integrate small, specialized design patterns (e.g., Factory, Builder, Adapter) into these templates. The AI can then instantiate these patterns accurately.

3. Using Java Modules and Dynamic Loading

3.1 The Power of module-info.java

As an example, a Java module-info.java file can delineate which packages are exported and what dependencies are required. Generative AI can produce skeleton module-info.java files based on your project structure:

This ensures each module is self-describing and loosely coupled—you can dynamically load or unload modules at runtime (with additional frameworks like OSGi or custom class loaders).

3.2 Dynamic Loading with Spring Boot & Groovy

Combining Spring Boot with Groovy can offer powerful runtime flexibility:

• Spring Boot enables easy microservice packaging and dependency injection.
• Groovy lets you script and dynamically load classes or configurations without recompiling the entire application.

AI can automatically generate these Groovy scripts to load modules contextually:

When your AI engine scaffolds this code, you drastically reduce the manual effort in setting up dynamic loading.

4. Generative AI Pipeline: From Templates to Custom IP Blending

Below is a PlantUML diagram illustrating a high-level generative AI pipeline that takes in your boilerplate templates and custom IP, then outputs ready-to-use modules:

1. Developer prompts the AI for a new service or feature.
2. The Generative AI Engine references Boilerplate Templates (e.g., your Spring Boot skeleton, Java ?module definitions) and merges them with Custom IP (special logic, domain-specific code).
3. The final code is generated and returned to the Developer—often as a ready-to-run or nearly complete project.

5. The Role of Reverse Engineering and CRTP Creative Solutions

5.1 Reverse Engineering Approaches

Not all of your existing legacy systems are well-documented. By using reverse engineering:

• You can generate UML diagrams or sequence diagrams from existing codebases.
• The AI can learn patterns from your current architecture to create consistent scaffolding.

5.2 CRTP Creative Solutions

We’ve discussed the CRTP (Curiously Recurring Template Pattern) in the context of creative, templated solutions:

• In C++: CRTP is a technique where a base class uses a derived class as a template parameter.
• In Java/Groovy: You can replicate similar patterns by having abstract or generic classes that serve as scaffolding for derived components.

Your generative AI can leverage these patterns to craft domain-specific abstract classes, ensuring every new service adheres to company-wide best practices.

6. Practical Tips for Effective AI-Assisted Development

1. Version Control Integration Set up a dedicated branch or code area where AI-generated code is merged. Enforce Pull Request reviews for human oversight.
2. Automated Testing & Validation Bake unit, integration, and functional tests into your AI templates. Tools like JUnit or Spock (for Groovy) can be added to the scaffold automatically.
3. Reusable Build Scripts Pre-generate Gradle or Maven scripts that align with your internal artifact repository and CI/CD pipelines.
4. Documentation and Comments Prompt your AI to insert docstrings and developer comments. Make sure the AI is generating code that is both functional and understandable.
5. Security Checks Integrate static analysis or security scanning (e.g., SonarQube) into your generated scaffolding. Ensure that secrets or credentials are never hard-coded.

7. Bringing It All Together

This Generative AI approach completes the triad from our first two articles:

1. Open-Source Delivery Model Sharing boilerplate templates and generative pipelines fosters collaboration across teams.
2. Modular Architecture The dynamic loading of Java modules and Spring Boot microservices ensures your system remains flexible and future-proof.
3. AI-Generated Boilerplate Offloading repetitive coding tasks to AI can cover up to 70% of your code scaffolding. Custom IP blending allows you to retain your unique competitive edge and domain expertise.

Done right, this synergy not only accelerates product delivery but also empowers engineering teams to focus on creative problem-solving, innovation, and value-adding features.

The following is one possible template to layout the proof of concept that your organization or group could setup:

Executive Summary/Narrative

Title:?Automated Code Generation and Custom IP Integration Process

Brief:?This project aims to develop an engineering process that leverages advanced code generation techniques and integrates proprietary intellectual property (IP) with generative code. By automating 70% of the application code generation through AI and curated templates, the process allows feature teams to focus on the critical 30% of custom code that constitutes the core IP. The generated services will be modular, utilizing concepts from C++ STL, CRTP, and Java modules, ensuring a clear separation of concerns between interface and implementation.

Problem Statement:?Modern enterprise applications, especially those comprising numerous microservices and APIs, require significant boilerplate code that is often repetitive and resource-intensive to produce manually. This approach can lead to inefficiencies, slower development cycles, and a lack of standardization across services. Additionally, the need to integrate custom IP into these services poses challenges in maintaining modularity and ensuring code quality.

Vision of the Approach:?The proposed process automates the generation of boilerplate code (70% of the application) using AI-assisted templates based on agreed-upon Spring Boot Service Patterns. YAML files describing each service's functionality will guide the generation process, ensuring consistency and alignment with the application's architectural goals. The remaining 30% of the code, which is the proprietary IP, will be handcrafted and integrated using design patterns like CRTP and Visitor, with a focus on modularity as outlined in the C++ STL concepts. This separation allows for the flexibility to update and maintain the custom IP without impacting the boilerplate code.

Desired Outcome(s):

• Efficiency:?Significant reduction in time and effort required to produce boilerplate code, leading to faster development cycles.
• Consistency:?Standardized service patterns across the application, ensuring uniformity in code structure and quality.
• Modularity:?Clear separation between interface and implementation, allowing for easy updates and maintenance of custom IP.
• Scalability:?The process should be scalable to accommodate additional services and APIs without compromising quality.
• Innovation:?Continuous improvement of templates and processes through AI and collaborative curation by engineers.

Definition of Done:

• Strategic Alignment:?The process must contribute to innovation, efficiency, quality, and collaboration, supporting the organization's strategic goals.
• Quality Assurance:?Generated code must pass predefined quality benchmarks, including performance metrics, code reviews, and automated testing.
• Documentation:?Complete technical specifications, user guides, and internal knowledge transfer materials must be provided for each phase.
• Stakeholder Sign-off:?Key stakeholders must review and approve each phase before proceeding.
• Resource Efficiency:?The process must demonstrate efficient use of time, budget, and manpower.

Feature: Automated Code Generation and Custom IP Integration

Title:?Automated Code Generation for Spring Boot Microservices

Description:?This feature focuses on automating the generation of boilerplate code for Spring Boot microservices using AI-curated templates. The goal is to generate 70% of the application code based on YAML definitions provided by feature teams, with the remaining 30% of the code being custom IP that is manually integrated. The process will utilize modular design principles, ensuring a clear separation between interfaces and implementations, and leverage Jenkins pipelines to automate the generation and integration process.

Problem Statement:?Manual code generation for each microservice is time-consuming and prone to inconsistencies. Integrating custom IP with generative code requires a robust process to maintain modularity and code quality.

Vision of the Approach:?By using AI to generate code from curated templates and YAML files, this feature will automate the bulk of the code generation process, allowing developers to focus on custom IP integration. The use of modular design principles will ensure that the custom IP can be updated and maintained independently of the boilerplate code.

Desired Outcome(s):

• Automated generation of 70% of the application code.
• Standardized service patterns across microservices.
• Modular integration of custom IP using modern design patterns.
• Improved development efficiency and consistency.

Definition of Done:

• All generated services meet quality benchmarks and pass automated tests.
• Complete documentation is available for the generation process.
• Stakeholders approve the generated code and integrated custom IP.
• Efficient use of resources throughout the process.

Example Stories

Story 1: Generate Boilerplate Code for a New Microservice

Tasks:

• Load the YAML file defining the new service.
• Select the appropriate Spring Boot template from the repository.
• Replace tokens in the template based on the YAML file.
• Generate the boilerplate code and save it to the repository.

Examples:

• Generating a basic CRUD service for managing user data.
• Creating a service that interacts with an external API.

Definition of Done:

• The boilerplate code is generated and committed to the repository.
• The code passes all automated unit tests.
• Documentation is updated to reflect the generated service.

Story 2: Integrate Custom IP with Generated Service

Tasks:

• Load the custom IP code and intersect it with the generated service.
• Apply design patterns (e.g., CRTP, Visitor) to ensure modularity.
• Compile the code with the necessary modules included at runtime.
• Run integration tests to validate the service.

Examples:

• Adding custom authentication logic to a generated user service.
• Integrating a proprietary algorithm for data processing within a generated service.

Definition of Done:

• The custom IP code is successfully integrated with the generated service.
• The service passes all integration tests.
• Documentation is updated with details on the custom IP integration.

Story 3: Enhance Template for Service Patterns

Tasks:

• Analyze existing Spring Boot service patterns.
• Collaborate with AI to enhance and optimize the templates.
• Update the repository with the new templates.
• Validate the templates by generating a sample service and running tests.

Examples:

• Optimizing a template for data layer services to improve performance.
• Adding a new template for orchestration services with built-in error handling.

Definition of Done:

• The new templates are added to the repository.
• A sample service is generated and passes all tests.
• Documentation is updated with details on the new templates.

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One of the key enablers of this approach is to ensure that you have an architecture and physical design that cleanly separates interface from implementation.? Without this concept in place, you reduce you chance at success.? The generative code must be 100% regenerative between each build and it must not compromise the usage of the proprietary code or, worse, require that the proprietary code be generated using a public LLM.

Recap: Interface and Implementation Separation

In the original conversation, the goal was to mandate that the application and domain code be separated into different modules while adhering to the interface separation concept. This meant:

1. Interface (plus factory) in one module: Exports interfaces, abstract factories, or any contract definitions that the rest of the system depends on.
2. Implementation in a separate module: Contains concrete classes that implement those interfaces. Not directly accessible to the main application except via a ServiceLoader or some dynamic discovery mechanism.
3. Spring Boot REST Application: Consumes the interface module. Uses a custom ServiceLoader (or the built-in Java ServiceLoader) to dynamically load the implementation module(s).

By structuring your modules this way, you ensure clear boundaries between domain/application logic (IP code) and the supporting “plumbing” or boilerplate. The main advantage is that you can swap or update implementations without altering the interfaces or the Spring Boot app itself.

Why This Matters for 70/30 Boilerplate vs. Custom IP

• 70% Boilerplate Code: Templates for controllers, repositories, integration classes, basic DTOs, etc. are repeatedly generated.
• 30% Custom IP: Handcrafted logic that encodes unique domain rules, algorithms, or specialized behaviors.

By separating interfaces from implementations, you give your AI-based or templating system a stable target to generate (or regenerate) the boilerplate in the implementation module without tangling it with your IP-laden interface or domain logic. The interface module remains clean, focusing on contracts and domain abstractions.

The following is a template for building out a POC with these capabilities:

Executive Summary

Title: Modular Service Architecture with Feature-Driven Module Loading

Brief:?This initiative aims to redesign the current service architecture to enhance modularity, flexibility, and scalability, allowing multiple teams to work simultaneously on different features within the same service. The focus is on separating domain and application logic from Spring Boot technology, enabling dynamic module loading based on feature flags received via HTTP headers.

Description:

Problem Statement:?The current monolithic service architecture can be heavily intertwined with Spring Boot code, making it difficult to manage and scale as multiple teams work on different features simultaneously. Feature flags are currently managed through YAML files, requiring redeployments for changes, and resulting in a scattered implementation of feature logic across the codebase.

Vision of the Approach:?The proposed architecture will introduce strict separation between the application/domain code and the Spring Boot technology layer, leveraging architecture principles. Feature flags will be managed via HTTP headers, enabling dynamic loading of specific module versions at runtime based on the feature flag. This modular approach allows for forward and backward compatibility, supporting multiple versions of the same service simultaneously.

Desired Outcome(s):

• Improved modularity, enabling independent development and deployment of service components.
• Dynamic module loading based on feature flags, reducing the need for redeployments.
• Simplified codebase with clear separation of concerns, enhancing maintainability.
• Support for multiple versions of a service to coexist, facilitating feature-driven development.

Definition of Done:

• Strategic Alignment:?The new architecture must support the strategic goals of innovation, efficiency, and collaboration, allowing teams to work independently on different features.
• Quality Assurance:?The implementation must pass rigorous code reviews, performance testing, and demonstrate backward and forward compatibility.
• Documentation Completion:?Comprehensive documentation must be provided, including architecture diagrams, usage guides, and internal knowledge transfer materials.
• Stakeholder Sign-off:?Approval from key stakeholders must be obtained, ensuring the architecture meets business and technical requirements.
• Resource Efficiency:?The project must be completed within the allocated budget and timeline, demonstrating efficient resource use.

Feature: Modular Service Design with Feature-Driven Loading

Description:

This feature focuses on restructuring the existing service into a modular architecture, enabling dynamic module loading based on feature flags passed via HTTP headers. The goal is to improve the scalability and maintainability of the service, allowing multiple teams to work on different features independently.

Functionality to Create:

• Separate the domain and application logic into distinct modules, decoupled from the Spring Boot technology layer.
• Implement a dynamic module loader that can load specific versions of modules based on feature flags.
• Replace the YAML-based feature flag management with HTTP header-based flags, enabling on-the-fly configuration changes without redeployment.

Examples to Convey Understanding:

• Example 1:?For a feature related to a new order processing logic, the feature flag "epic1::featureA" will load the?OrderServiceImplV1?module.
• Example 2:?For a feature that enhances the billing system, the feature flag "epic2::featureB" will load the?BillingServiceImplV2?module.

Physical Deliverables (Definition of Done):

• A fully functional modular architecture that separates domain, application, and technology layers.
• A dynamic module loader integrated with the service to load implementations based on feature flags.
• Comprehensive documentation covering the new architecture, including usage guidelines and examples.
• A demo session with the development team to showcase the new architecture and its capabilities.

Related Stories

Story 1: Separation of Domain and Application Logic

What I Intend to Do:

• Refactor the existing codebase to extract domain and application logic into separate modules.
• Remove all Spring-specific annotations from the domain and application layers.
• Ensure that the domain and application logic can function independently of Spring Boot.

Examples:

• Refactoring an order processing service to remove?@Autowired?annotations and replace them with constructor-based dependency injection.
• Creating a module for domain entities that can be reused across different implementations.

Definition of Done:

• Domain and application modules are fully separated and decoupled from Spring Boot.
• All Spring-specific code is confined to the technology layer, with domain and application logic remaining framework-agnostic.
• Code passes all unit and integration tests, and documentation is updated to reflect the new structure.

Story 2: Implement Dynamic Module Loader

What I Intend to Do:

• Develop a custom?ServiceLoader?to dynamically load module implementations based on feature flags.
• Ensure that the module loader is capable of loading different versions of the same service simultaneously.
• Integrate the module loader with the Spring Boot application to respond to feature flags in HTTP headers.

Examples:

• Implementing a?ServiceLoader?that selects?OrderServiceImplV1?when the feature flag "epic1::featureA" is present.
• Demonstrating the ability to switch between?OrderServiceImplV1?and?OrderServiceImplV2?without redeploying the service.

Definition of Done:

• The dynamic module loader is functional and can load different versions of modules based on feature flags.
• The Spring Boot application successfully integrates with the module loader and responds to feature flags in HTTP headers.
• All tests pass, and the system is verified to support multiple versions of modules in production.

Story 3: HTTP Header-Based Feature Flag Management

What I Intend to Do:

• Replace the current YAML-based feature flag management with a system that uses HTTP headers.
• Implement logic to parse feature flags from HTTP headers and map them to specific module versions.
• Ensure that feature flags can be updated on the fly without requiring a redeployment.

Examples:

• Parsing the "X-Feature-Flag: epic1::featureA" HTTP header to load the corresponding module version.
• Implementing fallback logic to load a default module version if no feature flag is present.

Definition of Done:

• Feature flags are successfully managed via HTTP headers, and the service responds dynamically without redeployment.
• The system correctly maps feature flags to module versions and loads the appropriate implementations.
• Documentation and examples are provided to demonstrate the new feature flag management approach.

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Conclusion and Next Steps

• Real-World Trials: Start small and let AI generate a single new microservice or module, then expand once the process proves viable.
• Refine and Iterate: Continuously update your boilerplate templates and training data with the latest best practices.
• Cultural Embrace: Just like adopting open-source or modular principles, effectively deploying AI requires organizational buy-in and iterative learning.

Call to Action

• Comment Below: Share your experiences or questions around AI-generated code.
• Let’s Collaborate: If you have a unique IP or domain logic, how might you blend it with generative templates to gain a productivity edge?
• Stay Tuned: Future explorations in this space may dive deeper into advanced “AI-first” architecture patterns and the latest frameworks supporting generative solutions.

Author’s Note: We are at a pivotal moment in software engineering—where AI, open-source culture, and modular architecture converge. By embracing generative AI for repetitive tasks, organizations can drastically reduce friction and deliver more robust products. As always, adapt these insights to your environment, validate them through pilots, and push the boundaries of what is possible in modern product development.

Happy coding, and may your boilerplate be forever AI-assisted!