Methodloops

Methodloops are a programming pattern in which a loop iterates over a collection of method references and applies each to an input, often to transform or validate the data. The collection can be predefined or discovered at runtime through reflection or metadata. In a typical methodloop, data is passed from one method to the next in sequence, with each step performing a discrete operation.

This pattern supports extensibility because new steps can be registered without modifying the core loop. It is common in plugin architectures, testing frameworks, and data processing pipelines where multiple validation, transformation, or enrichment steps are applied to each item.

Implementation approaches vary by language. In languages with first-class functions, a list or array of callables can be iterated, applying each to the current data. In other languages, method references or reflective invocations can be used to assemble the sequence. A simple example in pseudocode:

data = initial_input

methods = [validate, normalize, enrich]

for f in methods:

data = f(data)

Advantages of methodloops include modularity, ease of extension, and clear separation of concerns between steps. Drawbacks can include runtime overhead from dynamic dispatch, potential difficulty in static analysis, and the need for careful contract design between successive methods to avoid unintended data loss or side effects.

Related concepts include function pipelines, the chain of responsibility, and plugin architectures. Methodloops emphasize sequential, composable application of discrete methods to a single data item or stream.