Abstract. Data in object-oriented programming is organized in a hierarchy of classes. The problem of object-oriented pattern matching is how to explore this hierarchy from the outside. This usually involves classifying objects by their run-time type, accessing their members, or determining some other characteristic of a group of objects. In this paper we compare six different pattern matching techniques: object-oriented decomposition, visitors, type-tests/type-casts, typecase, case classes, and extractors. The techniques are compared on nine criteria related to conciseness, maintainability and performance. The paper introduces case classes and extractors as two new pattern-matching methods and shows that their combination works well for all of the established criteria.

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Pattern matching makes ML programs more concise and readable, and these qualities are also sought in object-oriented settings. However, objects and classes come with open class hierarchies, extensibility requirements and the need for data abstraction, which all conflict with matching on concrete data types. Extractor-based pattern matching has been proposed to address this conflict. Extractors are user-defined methods that perform the task of value discrimination and deconstruction during pattern matching. In this paper, we give the first formalization of extractor-based matching, using a first-order object-oriented calculus. We give a direct operational semantics and prove it sound. We then present an optimizing translation to a target language without matching, and prove a correctness result stating that an expression is equivalent to its translation.

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