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Evolvability of Architectural Styles

Software architecture exists at least partially to enable certain types of evolution across specific dimensions — easier change is one of the reasons for architecture patterns. Different architectural patterns have different inherent quantum sizes, which impact their ability to evolve. In this section, we investigate several popular architecture patterns and evaluate their inherent quantum size, along with their impact on the architecture’s natural ability to evolve based on our three evolutionary criteria: incremental change, fitness functions, and appropriate coupling.

Note that while the architectural pattern is critical for successful evolution, it isn’t the only determining factor. The inherent characteristics of the pattern must be combined with the additional characteristics defined for the system to fully define the dimensions of evolvability.

Big Ball of Mud

First, consider the degenerate case of a chaotic system with no discernible architecture, colloquially known as the Big Ball of Mud antipattern. While typical architectural elements like frameworks and libraries may exist, developers haven’t built structure on purpose. These systems are highly coupled, leading to rippling side effects when changes occur. Developers created highly coupled classes with poor modularity. Database schemas snaked into the UI and other parts of the system, effectively insulating them against change. DBAs spent the last decade avoiding refactoring by stitching together tightly bound join tables. Likely driven by draconian budget constraints, operations crams as many systems together as possible and deals with the operational coupling.

Figure 4-3 shows a class coupling diagram that exemplifies the Big Ball of Mud: each node represents a class, the lines represent coupling (either inward or outward) and the boldness of the line indicates the number of connections.

Afferent and efferent coupling for a dysfunctional architecture

Figure 4-3. Afferent and efferent coupling for a dysfunctional architecture

Changing any part of the application depicted in Figure 4-3 (taken from a real project) presents intense challenges. Because so much exuberant coupling exists between classes, it is virtually impossible to modify one part of the application without impacting other parts. Thus, from an evolvability standpoint, this architecture scores extremely low. Developers who need to change data access throughout the application must hunt down all the places it exists and change them, risking missing some places.

From an evolution standpoint, this architecture fails each criteria drastically: Incremental change

Making any change in this architecture is difficult. Related code is scattered throughout the system, meaning changes to one component will cause unexpected breakages in other components. Fixing those breakages will generate more breakage, a rippling effect that never ends.

Guided change with fitness functions

Building fitness functions for this architecture is difficult because no clearly defined partitioning exists. To build protective functions, developers must be able to identify parts to protect, and no structure exists in this architecture outside low-level functions or classes.

Appropriate coupling

This architectural style is a good example of inappropriate coupling. No architectural advantages result from building software like this.

In this dire state, change is difficult and expensive. Essentially, because each part of the system is highly coupled to every other part, the quantum is the entire system — no part is easy to change because every part affects every other part.

 
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