Dynamic Pattern Matching with Python (DLS 2020 - Dynamic Languages Symposium) - DLS 2020

Sun 15 - Fri 20 November 2020 Online Conference

co-located with SPLASH 2020

Who

Tobias Kohn, Guido van Rossum, Brandt Bucher, Talin, Ivan Levkivskyi

Track

Time Zone

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When

Wed 18 Nov 2020 15:20 - 15:40 at SPLASH-III - 4 Chair(s): Antoine Miné, Jeremy G. Siek
Thu 19 Nov 2020 03:20 - 03:40 at SPLASH-III - 4 Chair(s): Shigeru Chiba, Caterina Urban

Abstract

Pattern matching allows programs both to extract specific information from complex data types, as well as to branch on the structure of data and thus apply specialized actions to different forms of data. Originally designed for strongly typed functional languages with algebraic data types, pattern matching has since been adapted for object-oriented and even dynamic languages. This paper discusses how pattern matching can be included in the dynamically typed language \emph{Python} in line with existing features that support extracting values from sequential data structures.

Link to Publication

http://design.cs.iastate.edu/splash20/dls20/dls20main-p12-p.pdf

DOI

https://doi.org/10.1145/3426422.3426983

Tobias Kohn

University of Cambridge, UK

United Kingdom

Guido van Rossum

Python Software Foundation

Brandt Bucher

Research Affiliates, LLC

United States

Talin

Ivan Levkivskyi

Dropbox Ireland

Media

Time Zone

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Time Band

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Session Program

Wed 18 Nov
Displayed time zone: Central Time (US & Canada) change

	15:00 - 16:20	4DLS 2020 / SAS at SPLASH-III +12h Chair(s): Antoine Miné Sorbonne Université, Jeremy G. Siek Indiana University, USA

	15:00 20m Research paper		Memory-Efficient Fixpoint ComputationArtifact SAS Sung Kook Kim University of California, Davis, Arnaud J. Venet Facebook, Aditya V. Thakur University of California, Davis Pre-print Media Attached
	15:20 20m Talk		Dynamic Pattern Matching with Python DLS 2020 Tobias Kohn University of Cambridge, UK, Guido van Rossum Python Software Foundation, Brandt Bucher Research Affiliates, LLC, Talin , Ivan Levkivskyi Dropbox Ireland Link to publication DOI Media Attached
	15:40 20m Research paper		Simple and Efficient Computation of Minimal Weak Control ClosureArtifact SAS Abu Naser Masud Malardalen University Media Attached File Attached
	16:00 20m Talk		Sound, Heuristic Type Annotation Inference for Ruby DLS 2020 Milod Kazerounian University of Maryland, College Park, Brianna M. Ren University of Maryland, Jeffrey S. Foster Tufts University Link to publication DOI Pre-print Media Attached

Thu 19 Nov
Displayed time zone: Central Time (US & Canada) change

	03:00 - 04:20	4SAS / DLS 2020 at SPLASH-III Chair(s): Shigeru Chiba The University of Tokyo, Caterina Urban École normale supérieure

	03:00 20m Research paper		Memory-Efficient Fixpoint ComputationArtifact SAS Sung Kook Kim University of California, Davis, Arnaud J. Venet Facebook, Aditya V. Thakur University of California, Davis Pre-print Media Attached
	03:20 20m Talk		Dynamic Pattern Matching with Python DLS 2020 Tobias Kohn University of Cambridge, UK, Guido van Rossum Python Software Foundation, Brandt Bucher Research Affiliates, LLC, Talin , Ivan Levkivskyi Dropbox Ireland Link to publication DOI Media Attached
	03:40 20m Research paper		Simple and Efficient Computation of Minimal Weak Control ClosureArtifact SAS Abu Naser Masud Malardalen University Media Attached File Attached
	04:00 20m Talk		Sound, Heuristic Type Annotation Inference for Ruby DLS 2020 Milod Kazerounian University of Maryland, College Park, Brianna M. Ren University of Maryland, Jeffrey S. Foster Tufts University Link to publication DOI Pre-print Media Attached