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How software architecture changes in the era of AI

In the world of software development, architecture plays a crucial role in shaping the overall structure and design of a system. Traditionally, architecture discussions have followed an outside-in approach, where architects and decision-makers envision the future architecture and communicate their ideas to the development team. However, this standard approach often falls short of delivering the desired results. In this blog post, we explore the limitations of the outside-in approach and propose alternative strategies to improve the architecture design process.


The Pitfalls of the Outside-In Approach

  1. Starting from the blank canvas every time: In reality, software development rarely involves starting from scratch. Instead, we build upon existing architectures with the goal of enhancing and improving them. When architects and decision-makers attempt to invent the future architecture without sufficient familiarity with the current codebase, their assumptions may be inaccurate or flawed. This lack of context can lead to suboptimal design decisions that hinder the development process.

  2. Communication Gaps: Effective communication between decision-makers and engineers is vital for successful architecture implementation. However, when architects pass down architecture requirements through documentation or verbal means, information can be lost or misinterpreted along the way. This breakdown in communication can result in a misalignment between the envisioned architecture and its actual implementation.

  3. Lack of Feedback Loop: One significant drawback of the outside-in approach is the absence of a proper feedback loop. Architects, team leads, and other stakeholders often lack a non-intrusive method to validate and review the implemented architecture. As a result, mistakes and issues may only be identified late in the development cycle, leading to costly and time-consuming rework.

Redefining the Architecture Design Process

  • Generating High-Level Architecture from Code: To overcome the limitations of the outside-in approach, one alternative is to generate the architecture directly from the codebase. The code itself represents the ultimate source of truth, providing insights into the existing structure, dependencies, and patterns. By analyzing the codebase automatically, engineers can gain a deeper understanding of the architecture and make informed decisions for further enhancements. Some tools are getting close to doing so. For example, architecture-view-nestjs VS code extension generates 2 levels of architecture for NestJs applications.


  • Leveraging AI for Architecture Generation: Then, we can enhance the deduced architecture by leveraging artificial intelligence (AI). With the wealth of existing architectural patterns and best practices, training AI models like GPT on this knowledge can enable them to generate new and innovative architectural solutions. By tapping into AI's capabilities, architects can benefit from an augmented design process, expanding the scope of possibilities and ensuring comprehensive exploration of potential architectures.

  • Real-Time Architecture Updates: To facilitate better collaboration and feedback, it is essential to establish a near real-time architecture view. This view would enable architects, team leads, and stakeholders to monitor the progress of architecture implementation throughout the development process. By having a clear and up-to-date understanding of the evolving architecture, early detection of implementation issues becomes possible, allowing for timely intervention and course correction.


Conclusion

The standard outside-in approach to architecture discussions often falls short due to its detachment from the existing codebase, communication gaps, and the lack of a feedback loop. By embracing alternative strategies, such as deducing architecture from code, leveraging AI for generating architectural solutions, and establishing real-time architecture updates, we can revolutionize the architecture design process. These new approaches encourage a more holistic understanding of the system, enable better collaboration between decision-makers and implementers, and empower teams to detect and rectify issues early on. Ultimately, by reimagining how we approach architecture discussions, we can unlock the potential for more robust, efficient, and successful software systems.

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