BibTeX
@MISC{Cannon05localizedtype,
author = {Brett Cannon},
title = {Localized Type Inference of Atomic Types in Python},
year = {2005}
}
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Abstract
Types serve multiple purposes in programming. One such purpose is in providing information to allow for improved performance. Unfortunately, specifying the types of all variables in a program does not always fit within the design of a programming language. Python is a language where specifying types does not fit within the language design. An open source, dynamic programming language, Python does not support type specifications of variables. This limits the opportunities in Python for performance optimizations based on type information compared to languages that do allow or require the specification of types. Type inference is a way to derive the needed type information for optimizations based on types without requiring type specifications in the source code of a program. By inferring the types of variables based on how control and other hints in a program, the type information can be derived and used in a constructive manner. This thesis is an exploration of implementing a type inference algorithm for Python without changing the semantics of the language. It also explores the benefit of adding type annotations to method calls in order to garner more type information.
Keyphrases
type inference type information atomic type type specification source code open source performance optimization type inference algorithm language design needed type information programming language dynamic programming language multiple purpose improved performance constructive manner type annotation
