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Home arrow Economics arrow Building Evolutionary Architectures: Support Constant Change
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Static Versus Dynamic

Static fitness functions have a fixed result, such as the binary pass/fail of a unit test. This type encompasses any fitness function that has a predefined desirable value: binary, a number range, set inclusion, and so on. Metrics are often used for fitness functions. For example, an architect may define acceptable ranges for average cyclomatic complexity of methods in the code base, graded upon checkin using a metrics tool wired into the deployment pipeline.

Dynamic fitness functions rely on a shifting definition based on extra context. Some values may be contingent on circumstances, and most architects will accept lower performance metrics when operating at high scale. For example, a company might build a sliding value for performance based on scalability — more scale means slower performance is permitted, but only within a range.

Automated Versus Manual

Clearly, architects like automated things — part of incremental change includes automation, which we delve into deeply in Chapter 3. Thus, it’s not surprising that developers will execute most fitness functions within an automated context: continuous integration, deployment pipelines, and so on. Indeed, developers and DevOps have performed a tremendous amount of work under the auspices of Continuous Delivery to automate many parts of the software development ecosystem previous thought impossible. This beneficial trend should continue.

However, as much as we’d like to automate every single aspect of software development, some aspects of software resist automation. Sometimes, a critical dimension within a system, such as legal requirements, defies automation. For example, developers building applications in some problem domains must have manual certification for changes for legal reasons, which cannot be automated away. Similarly, a project may have aspirations to become more evolutionary evolutionary but not yet have appropriate engineering practices in place. For example, perhaps most QA is still manual on a particular project and must remain so for the near future. In both these cases (and others), we need manual fitness functions that are verified by a person-based process.

Clearly, the path to better efficiency eliminates as many manual steps as possible, but many projects still require necessary manual procedures. We still define fitness functions for those characteristics and verify them using manuals stages in deployment pipelines (covered in more detail in Chapter 3).

 
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