Showing posts with label Programming. Show all posts
Showing posts with label Programming. Show all posts
Friday, November 12, 2010
Tuesday, January 12, 2010
Tuesday, January 5, 2010
Towards a Standard Parser Generator
Abstract
Developing parsers for "little" languages is a common task for many software developers. People have frequently requested inclusion of a specific parser generator framework into the Python library. In this paper, we compare several Python parser generators, using the XPath language as an application. Criteria for comparison are ease of use, performance, and ease of deployment. Using these criteria, we give a recommendation for including parser generators into the standard library.. Keywords: Parsers, XPath, YAPPS, Spark, BisonGen
Algorithm Education in Python
Abstract
Design and analysis of algorithms are a fundamental topic in computer science and engineering education. Many algorithms courses include programming assignments to help students better understand the algorithms. Unfortunately, the use of traditional programming languages forces students to deal with details of data structures and supporting routines, rather than algorithm design. Python represents an algorithm-oriented language that has been sorely needed in education. The advantages of Python include its textbook-like syntax and interactivity that encourages experimentation. More importantly, we report our novel use of Python for representing aggregate data structures such as graphs and flow networks in a concise textual form, which not only encourages students to experiment with the algorithms but also dramatically cuts development time. These features have been implemented in a graduate level algorithms course with successful results.
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Design and analysis of algorithms are a fundamental topic in computer science and engineering education. Many algorithms courses include programming assignments to help students better understand the algorithms. Unfortunately, the use of traditional programming languages forces students to deal with details of data structures and supporting routines, rather than algorithm design. Python represents an algorithm-oriented language that has been sorely needed in education. The advantages of Python include its textbook-like syntax and interactivity that encourages experimentation. More importantly, we report our novel use of Python for representing aggregate data structures such as graphs and flow networks in a concise textual form, which not only encourages students to experiment with the algorithms but also dramatically cuts development time. These features have been implemented in a graduate level algorithms course with successful results.
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Saturday, January 2, 2010
Monday, December 28, 2009
Eclipsesource.com - Persistent Trees in git, Clojure and CouchDB
This is a tale of three images. I found these images while investigating the internals of several different applications. There are some really neat software projects emerging at the moment, and as a developer I always find it interesting to take a look at the implementation details, because there is often a lot to be learned. It’s not always something you might need right now, but maybe a few years down the line you may be confronted with a similar problem. Plus – in my opinion – knowing a bit about the internals of a program helps reasoning about its behaviour.
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