My thesis, "Localized Type Inference of Atomic Types in Python", was successfully defended today for my MS in Computer Science at the California Polytechnic State University, San Luis Obispo. With that stamp of approval I am releasing it to the world. You can grab a copy at http://www.drifty.org/thesis.pdf . For those of you who attended my talk at PyCon 2005 this is the thesis that stemmed from the presented data. As of this exact moment I am not planning to release the source code mainly because it's a mess, I am not in the mood to pull the patches together, and the last thing I want happening is people finding mistakes in the code. =) But if enough people request the source I will take the time to generate a tar.bz2 file of patches against the 2.3.4 source release and put them up somewhere. Below is the abstract culled directly from the thesis itself. -Brett C. --------------------------------- 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 flow 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.