Java/Kotlin
Best practices for separating concerns between business rules and infrastructure code in Java and Kotlin to ease testing.
A practical guide to cleanly split business rules from infrastructure in Java and Kotlin, improving modularity, testability, and maintainability through disciplined layering, explicit boundaries, and resilient design choices across ecosystems.
Published by
Daniel Cooper
July 28, 2025 - 3 min Read
When software teams design systems, separating business rules from infrastructure concerns creates a durable boundary that survives changes in frameworks, libraries, and deployment environments. In Java and Kotlin, this separation is not merely a matter of organizing packages; it is a disciplined practice that guides how components interact, how data flows, and how tests exercise each layer. The business domain should carry the core logic in a portable form, while infrastructure code handles persistence, messaging, and integration details. This division supports clearer reasoning, easier refactoring, and more effective test double creation, which in turn reduces the risk of regressions when the surrounding stack evolves.
A practical approach begins with defining explicit contracts that separate what the system does from how it does it. In both Java and Kotlin, this often means introducing interfaces or pure functions for core rules and keeping implementation details in separate modules or packages. The domain layer remains platform-agnostic, expressing invariants and business decisions without referencing frameworks, databases, or external services. Infrastructure components then implement these contracts, translating domain actions into concrete operations. By keeping these boundaries visible in source trees, teams can tailor tests to each layer’s responsibilities, achieving precise evidence of correctness without conflating concerns.
Interfaces and adapters organize responsibilities and enable safe evolution.
The first step toward robust separation is to articulate domain models that express invariants and operations in terms of real-world concepts. In Kotlin, data classes and sealed classes can encode state transitions without tying them to persistence or messaging details. In Java, records and sealed types (where available) offer similar expressiveness, while interfaces define ports that decouple rules from adapters. By modeling the domain as a set of rules that can be evaluated, composed, and validated independently of how data is stored or transmitted, teams gain a portable core that remains stable when infrastructure evolves, upgrades occur, or new delivery channels appear.
Once a stable domain model exists, define use-case boundaries that orchestrate behavior without embedding infrastructure calls. Use cases encapsulate a sequence of domain operations, validations, and decision points, steering the flow from input to outcome. In both Java and Kotlin, this typically means a service or application layer that accepts input, delegates to domain services, and then delegates persistence or communication to adapters via interfaces. Tests for these boundaries exercise the orchestration using in-memory stubs or mocks, ensuring that the domain rules are exercised independently of external concerns. This approach yields clearer intent and simpler test design.
Concrete examples reduce ambiguity and accelerate onboarding.
Implementing a clean boundary between domain and infrastructure requires well-chosen interfaces that act as ports. The domain should depend only on abstractions, not on concrete implementations. In Java, you can model ports as interfaces with small, cohesive methods; in Kotlin, higher-order functions or interfaces can serve the same purpose, often with concise syntax. Infrastructure components implement these ports, providing persistence, queues, or external service calls. Tests substitute real implementations with fakes or mocks, proving that the domain behavior remains correct as long as the ports contract is honored. This pattern—domain, ports, adapters—supports seamless changes to technology stacks without disturbing core rules.
To keep adapters from leaking into domain logic, place anti-corruption layers (ACLs) or translator components at the boundary. The ACL ensures that data entering the domain is shaped correctly, while transforming domain results back into infrastructure-friendly forms for persistence or delivery. In Kotlin, extension functions and data classes can simplify translation layers, whereas Java users can centralize mappers or converters in dedicated packages. The key is to avoid sprinkling infrastructure concerns throughout domain services. When changes occur, teams can adjust translation rules, data schemas, or communication protocols without rewriting central business logic, preserving clarity and reducing risk.
Layering and dependency direction reinforce sustainable design.
Consider a simple e-commerce domain where the core rules govern discount eligibility, pricing strategies, and order validation. The domain model captures products, carts, and orders, enforcing invariants such as nonnegative totals and valid discounts. A separate repository port handles persistence, while an event bus port manages domain events. In Kotlin, you can implement the domain with sealed classes to represent state transitions and pure functions for calculations. In Java, use immutable value types and well-defined domain services. Tests confirm the correctness of rules by invoking domain methods with controlled inputs, independent of how data is stored or communicated to external systems.
For testing infrastructure, write dedicated integration tests that exercise adapters against real collaborators in a controlled environment. Mock or stub the domain once to validate orchestration, and then verify each adapter against its API contract. In Java and Kotlin, test doubles become simpler when the domain remains decoupled; you can verify that persisting an order triggers the appropriate domain rules without coupling to a database, message broker, or HTTP service. This separation yields reproducible tests and clearer failure signals, making it easier to locate whether a bug lies in business logic or in infrastructure wiring.
Concrete guidelines help teams apply these concepts consistently.
A reliable layering strategy enforces dependency direction from outer to inner layers. The domain layer should not reference infrastructure details; instead, outer layers implement interfaces defined by the domain. In Java, this often means placing domain interfaces in a core module and keeping infrastructure implementations in separate modules that depend on those interfaces. In Kotlin, you can leverage modularization and explicit dependencies to the same effect. Tests that exercise the domain rely on in-memory implementations, while end-to-end tests exercise the adapters. This architecture supports parallel development: domain specialists modify rules, while infrastructure engineers evolve adapters with minimal risk to the core logic.
Another practical practice is to keep side effects out of pure domain methods. Domain calculations, validations, and decision logic should be deterministic and free of IO. In Kotlin, refer to pure functions and immutable data, avoiding calls to network or file systems inside domain transitions. In Java, favor stateless services and value objects for domain operations, deferring any persistence or external interaction to dedicated components. When side effects do appear, wrap them behind clearly defined interfaces and ensure they do not alter the domain’s state machine or invariants. Such discipline eases reasoning and improves testability.
It helps to establish coding conventions that explicitly separate concerns. Create distinct modules for domain, application, and infrastructure, and enforce that domain code has no dependencies on the infrastructure module. Use dependency injection to supply adapters at runtime, rather than hard-coding dependencies inside domain services. In Kotlin, leverage constructor injection and interface-based contracts to keep test doubles straightforward. In Java, consider using frameworks that support clean DI patterns while avoiding framework-specific leakage into domain models. Document the desired boundaries and provide example layouts to speed up onboarding for new engineers joining the project.
Finally, maintain a culture of continuous refactoring toward clearer boundaries. Regularly review areas where responsibilities blur and extract interfaces or adapters as needed. Encourage test-driven discipline so that changes in infrastructure do not ripple into domain logic, and vice versa. Automate compilation and test runs to catch cross-layer issues early, and use modular builds to ensure fast feedback. Over time, this approach yields a codebase where business rules remain stable, infrastructure evolves with confidence, and testing remains focused, predictable, and productive for Java and Kotlin teams alike.
