Locality of Behavior: Rethinking Code Structure

Software development evolves through principles that shape how teams build and maintain systems. The longstanding doctrine of Separation of Concerns (SoC) has guided architects to divide code by technology layers—HTML for structure, CSS for styling, JavaScript for logic—promising clarity and organization. Yet, this approach often fragments codebases, creating complexity that hinders maintenance. A counter-movement, Locality of Behavior (LoB), prioritizes grouping related elements to make behaviors intuitive and self-contained. This shift reflects broader trends in modular design, influencing everything from frontend frameworks to enterprise architectures.

The Limits of Separation of Concerns

SoC emerged as a foundational principle to manage complexity by isolating concerns. In practice, it scatters code across files: a button's markup in one place, its styles in another, and its interactivity elsewhere. This separation demands developers trace connections through multiple layers, leading to what experts describe as "spooky action at a distance"—unpredictable interactions that complicate debugging and refactoring.

Consider a framework for understanding this: imagine code organization as a supply chain. SoC resembles a globalized model with specialized factories (files) for each component, requiring extensive logistics (imports and references) to assemble a feature. While efficient for initial production, it falters under change—altering one part ripples through the chain, increasing costs and delays. Data from developer surveys, such as those from Stack Overflow in 2025, reveal that teams using strict SoC report higher cognitive load, with onboarding times stretching into weeks due to the need for tribal knowledge about scattered logic.

This inefficiency extends beyond individual projects. In competitive tech landscapes, companies reliant on SoC-heavy architectures face slower iteration cycles, eroding their edge in fast-paced markets like fintech and cloud services. For instance, fintech platforms handling real-time transactions cannot afford delays from fragmented code; a single update might involve coordinating across dozens of files, amplifying error risks.

Trade-offs in Traditional Architectures

SoC aligns with principles like Don't Repeat Yourself (DRY), aiming to centralize reusable elements. However, this often sacrifices locality for abstraction, making it harder to grasp a feature's full behavior from a single view. Richard Gabriel's insights on maintenance emphasize that easy-to-understand code requires visibility within a small scope—SoC undermines this by enforcing artificial boundaries based on technology rather than functionality.

Emergence of Locality of Behavior

LoB flips the script by co-locating markup, styles, and logic within the same module, ensuring a feature's behavior is evident from that unit alone. Coined by Carson Gross, this approach draws from pragmatic engineering, where maintainability stems from intuitive structures. Instead of separating by technology, LoB organizes by features, creating self-contained components that developers can comprehend, modify, or delete without unintended side effects.

Visualize this as a modular toolkit versus a dispersed warehouse. In the LoB model, each tool (feature) bundles everything needed—hammer, nails, instructions—in one drawer. This contrasts with SoC's warehouse, where tools are categorized by type, forcing searches across aisles. Frameworks like React exemplify this by allowing components to encapsulate state, styles, and rendering logic, reducing bugs from misaligned dependencies.

Expert commentary reinforces LoB's advantages. Discussions in podcasts like Dead Code highlight how it lowers cognitive overhead, enabling faster onboarding and safer refactoring. Nate Weller's advocacy for "screaming architecture"—organizing files by domain features—aligns with LoB, promoting deeper modules and explicit state management, such as through state machines, to clarify transitions.

In AI and machine learning contexts, LoB supports modular pipelines where data processing, model logic, and visualization co-locate, streamlining workflows in cloud environments. Similarly, in fintech and Web3, where security and auditability matter, localized behaviors make it easier to isolate and verify smart contract interactions or transaction handlers.

Tools and Frameworks Driving the Shift

Modern tools accelerate LoB adoption. HTMX, developed by Carson Gross, extends HTML with behavioral attributes, embedding logic directly without heavy JavaScript overhead. This simplifies hypermedia applications, particularly in cloud infrastructures where lightweight clients reduce latency.

Tailwind CSS furthers this by enabling utility classes inline with markup, merging styles and structure. Combined with React or Vue, these create cohesive components that enhance developer productivity. Svelte takes it further with compile-time optimizations, bundling everything into efficient bundles.

From a business perspective, platforms like Vercel and Netlify invest in these ecosystems, recognizing that LoB-aligned tools lower barriers to entry for startups. In competitive dynamics, companies adopting LoB gain advantages in scalability: teams report reduced reliance on specialized knowledge, allowing broader talent pools and faster pivots in markets like AI-driven fintech.

Consider network effects here. As more developers embrace LoB through popular frameworks, ecosystems grow—libraries like Storybook and Bit.dev facilitate component sharing, creating virtuous cycles where modular, localized designs become the norm, pressuring laggards to adapt.

Implications for Business Models

LoB influences competitive strategies by prioritizing maintainability over initial setup. In cloud computing, where infrastructure as code demands rapid iterations, LoB reduces deployment risks. For AI firms, co-located behaviors in ML pipelines enable quicker experimentation, aligning with agile models that drive innovation.

Fintech and Web3 benefit from LoB's emphasis on explicitness, mitigating risks in decentralized systems. Blockchain projects, for example, can encapsulate smart contract logic with associated interfaces, improving audit trails and reducing vulnerabilities from scattered code.

Future Directions and Recommendations

LoB stands poised to redefine standards in software architecture. Predictions point to its extension into backend realms, such as microservices where features encapsulate across layers, and serverless setups that bundle logic with data handlers. Tooling will evolve—IDEs might enhance navigation for co-located code, while educational shifts teach LoB alongside SoC, emphasizing context over dogma.

Organizations should assess their codebases through a LoB lens: audit for scattered behaviors and refactor toward feature-centric modules. In AI and cloud strategies, integrate LoB to foster modular designs that scale with data volumes. For fintech, leverage it to build resilient systems that adapt to regulatory changes without widespread disruptions.

Key Takeaways

Locality of Behavior addresses SoC's shortcomings by fostering cohesive, maintainable code through feature co-location. Tools like HTMX and React embody this, offering productivity gains that translate to business advantages in competitive tech sectors. As trends converge toward modular architectures, embracing LoB positions teams for efficient scaling in cloud, AI, and fintech landscapes. The focus shifts from technological silos to intuitive, feature-driven designs, unlocking faster innovation and reduced maintenance burdens.