Assembly Theory and Software Engineering: Complexity, Modularity, and Self-Correction

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Innovation and efficiency are crucial in software engineering. In my pursuit of finding new and better ways to facilitate these crucial aspects of software engineering, I tend to look in different fields for inspiration and insight. Recently, I’ve been exploring assembly theory, inspired by a podcast I listened to, and I've noticed fascinating parallels with our field. Assembly theory, a concept rooted in understanding complex systems and how they evolve, provides a fresh perspective on how we can improve our software engineering practices. Inspired by these insights, I decided to share how principles from assembly theory can offer fresh perspectives on software engineering and our understanding of software engineering in a novel yet familiar way.

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What is Assembly Theory?

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Assembly theory, introduced by Lee Cronin and his team in 2017, focuses on understanding how complex structures and systems are built from simpler components. It examines the pathways through which these components come together to form higher-order structures, emphasizing the importance of both modularity and evolutionary processes. By studying these pathways, assembly theory seeks to uncover the fundamental principles governing the complexity and emergence of new properties in biological and chemical systems. However assembly theory does not detail the exact path that any component has taken in its lifecycle, but it will arrive at the shortest possible evolutionary path for that component to exist in its current form.

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Complexity Reduction

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In software engineering, complexity is one of our biggest challenges. The same is true for assembly theory. Assembly theory simplifies the understanding of complex structures, aiming to break them down into simpler, more manageable components. Similarly, in software engineering, we strive to reduce complexity in our design, architecture and code. Think of refactoring as breaking down a complex structure into simpler parts, making it easier to manage and understand.

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Refactoring, design patterns, and modular programming are key techniques in this process. They help maintain clarity and reduce errors by organizing code into smaller, reusable components. Just as assembly theory seeks to reduce complexity in biological systems, these software engineering practices aim to create cleaner, more efficient code, that is still (at least) as effective as before these processes took place.

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Modularity and Emergent Complexity

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In assembly theory, modularity is essential for creating sophisticated systems. This idea resonates deeply with software engineering, where modular design allows for flexibility and scalability. Consider the microservices architecture: each service functions independently yet contributes to the system's overall functionality. This mirrors how modular components in assembly theory lead to emergent complexity and innovation/evolution.

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By promoting modularity, we can manage complexity and foster creativity in our software projects. Microservices, API design, and modular programming allow us to build systems that are both robust and adaptable. This approach not only simplifies development but also enables us to respond more effectively to changing requirements. Additionally, it provides us with reusable building blocks that can be generated based on prior activities, significantly reducing the time and energy needed for creating ever more complex systems, whilst maintaining reliability, scalability and maintainability.

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Innovation and Evolution

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Innovation often starts from a single idea, much like evolution within assembly theory. A single change or new component can set off a chain reaction, leading to significant advancements and new capabilities. In software engineering, this can be seen when a simple new feature or a small improvement in a process leads to broader innovation and enhancement of the entire system.

And just like nature allows for experiments through evolution where ideas are given time, space and resources to be executed, we need to apply the same approach to software engineering ideas.

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Self-Correcting Mechanisms

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There is no such thing as a perfect system over any significant length of time. Thus, imperfect systems are inevitable, but assembly theory teaches us the value of self-correcting mechanisms. In software engineering, this translates to robust error handling, thorough testing, and continuous improvement through a multitude of software engineering best practices such as continuous integration/continuous deployment (CI/CD). Building these self-correcting mechanisms into our development processes ensures that our systems can adapt and improve over time, reducing the impact of imperfections.

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Automated testing, feedback loops, and error handling are crucial for maintaining software quality. These practices allow us to identify and correct issues quickly, ensuring that our systems remain reliable and performant. Just as assembly theory emphasizes self-correction in biological systems, these software engineering practices help us build resilient and adaptable software that can withstand the test of time.

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Test-Driven Development (TDD) and Assembly Theory

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Another parallel I see between assembly theory and software engineering is in the area of TDD. While assembly theory and Test-Driven Development (TDD) approach problem-solving in a very different way, they both aim to achieve robustness and reliability. Assembly theory focuses on the evolutionary process of building complexity from simple components, allowing for natural self-correction and adaptation. On the other hand, TDD starts with writing tests before code, ensuring that every piece of functionality is defined and verified from the outset. Even though these approaches seem contradictory, they are very alike in the sense that where tests in TDD ensure verification, reality itself ensures verification and validation of the results generated by assembly theory.

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TDD creates a safety net that catches errors early and often, fostering a cycle of continuous improvement. This method differs from the evolutionary approach of assembly theory, but both strategies prioritize building systems that are resilient and capable of handling change.

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Conclusion

By connecting the principles of assembly theory with software engineering methodologies, we can uncover new ways to approach our work. Embracing complexity reduction, modularity, innovation, and self-correction not only enhances our efficiency but also fosters a culture of innovation. Let's challenge ourselves to think beyond traditional boundaries and apply these insights to our software development practices.

Assembly theory and software engineering may seem worlds apart, but their principles intersect in fascinating ways. By drawing these parallels, we can inspire new approaches to managing complexity, driving innovation, and building resilient systems, modularly. Let's harness these insights to drive innovation and improve our engineering culture, paving the way for a future where software development is both efficient and creative.

I’m curious on what other parallels you can find between the two and how these insights may help us better understand our own field of software engineering.