Nearly 1 in 4 Kotlin Developers Wrestle with the Chaos of Expanding Codebases

22.8% of Kotlin developers face growing pains with expanding codebases our survey reveals.

As applications evolve, their codebases grow—not just linearly, but in complexity. This complexity manifests in various dimensions: an increase in the lines of code, the intertwining of functionalities, the proliferation of dependencies, and the overarching challenge of ensuring the code remains clean and maintainable.

Read what Kotlin devs had to say about the architectural headaches they face in 2024here 👇

Why Does Complexity Matter?

At its core, complexity is a barrier to scalability, maintainability, and efficiency. A complex codebase can slow down development cycles, introduce bugs, and make onboarding new developers a herculean task. In the long run, it increases technical debt, making future enhancements and updates a daunting task.

Kotlin Devs Speak

Our survey, conducted among a diverse group of 138 Kotlin developers, aimed to pinpoint the pressing challenges in application architecture. The finding was clear: managing the complexity of expanding codebases is a critical challenge for nearly a quarter of respondents. This is a reflection of a broader trend in application development, where the rapid pace of feature development often comes at the cost of simplicity and ironically, scalability.

Strategies to Combat Complexity

Developers adopt several strategies to manage and mitigate complexity:

• Modularization: Breaking down the application into smaller, manageable modules can help isolate functionality and reduce interdependencies.
• Clean Architecture: Adopting architectural patterns that emphasize separation of concerns and modular design can enhance maintainability.
• Code Reviews and Refactoring: Regular code reviews and refactoring sessions can help identify and address complexity hotspots early on.
• Automated Testing: Implementing a robust suite of automated tests can safeguard against unintended consequences of changes in a complex codebase.

How Architecture Observability Can Combat Complexity

Architecture observability can also be a strategic approach to combat complexity, especially in the context of expanding and evolving codebases like those commonly found in Kotlin development projects. Architecture observability goes beyond the traditional operational observability (which focuses on metrics, logs, and traces at the runtime level) to provide insights into the structure, dependencies, and health of the application's architecture:

1. Visibility into Architectural Complexity: Architecture observability tools can provide a visual representation of the system's architecture, highlighting complex interdependencies, overly coupled components, and potential architectural antipatterns. This visibility is crucial for understanding the sources of complexity within the application.

2. Monitoring Architectural Decay: Over time, as new features are added and the codebase evolves, architectural decay can set in, leading to increased complexity and technical debt. Observability can help monitor this decay, offering early warnings when the architecture deviates from its intended design or when complexity metrics exceed certain thresholds.

3. Facilitating Refactoring and Modularization Efforts: By making the architecture's structure and its complexities observable, developers can make informed decisions about refactoring efforts. Identifying the most coupled and complex components can guide modularization efforts, breaking down the codebase into more manageable, loosely coupled modules.

4. Enhancing Decision-Making: Architecture observability can provide empirical data that can inform decision-making processes. Understanding the impact of new features or architectural changes on the system's complexity can help teams prioritize technical debt reduction and maintainability improvements.

5. Improving Onboarding and Collaboration: For new team members or teams architecture observability tools can offer a quick way to understand the application's overall design and how different components interact. This improves collaboration and efficiency, especially in complex projects.

6. Predictive Analysis for Architectural Health: Some architecture observability tools incorporate AI and machine learning to predict potential issues based on current and historical architectural data. This predictive analysis can help prevent complexity from reaching unmanageable levels.

Importantly, fostering a culture that values clean architecture and continuous improvement is essential. Observability tools could provide the data, but it's up to the development teams to act on this information to manage complexity effectively.