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Your search keyword '"Tempero, E"' showing total 2 results
Start Over Author "Tempero, E" Topic computer.programming_language
2 results on '"Tempero, E"'

1. How Do Python Programs Use Inheritance? A Replication Study

Author

Matteo Orru, Roberto Tonelli, Michele Marchesi, Ewan Tempero, Sun J.,Reddy Y.R.,Bahulkar A.,Pasala A., Orru', M, Tempero, E, Marchesi, M, and Tonelli, R

Subjects
Inheritance, Java, business.industry, Programming language, Computer science, Multiple inheritance, Empirical Studie, Software_PROGRAMMINGTECHNIQUES, Python (programming language), computer.software_genre, Empirical research, Software, sort, Software system, Metric, business, computer, Python, computer.programming_language
Abstract

In this work we present an empirical study on the use of inheritance in a curated corpus of Python systems. Replicating a study preformed on Java, we analyzed a collection of 51 software systems written in Python, and investigated how inheritance is effectively used by Python developers in practice through a convenient set of inheritance metrics. Our results suggest that on average fewer classes inherit from other classes than in Java, but more classes are inherited from. We also see a sort of symmetry relating the number of ancestors and the number of descendants in each system.

Published
2015
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2. A curated benchmark collection of python systems for empirical studies on software engineering

Author

Roberto Tonelli, Michele Marchesi, Ewan Tempero, Giuseppe Destefanis, Matteo Orru, Orru', M, Tempero, E, Marchesi, M, Tonelli, R, and Destefanis, G

Subjects
business.industry, Computer science, Empirical Studie, Python (programming language), Data science, Empirical research, Software, Package development process, Software system, Curated Code Collection, Software engineering, business, computer, computer.programming_language, Python
Abstract

The aim of this paper is to present a dataset of metrics as- sociated to the first release of a curated collection of Python software systems. We describe the dataset along with the adopted criteria and the issues we faced while building such corpus. This dataset can enhance the reliability of empirical studies, enabling their reproducibility, reducing their cost, and it can foster further research on Python software.

Published
2015

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