University of Hertfordshire

A curated benchmark collection of python systems for empirical studies on software engineering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

A curated benchmark collection of python systems for empirical studies on software engineering. / Orrú, Matteo; Tempero, Ewan; Marchesi, Michele; Tonelli, Roberto; Destefanis, Giuseppe.

11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015. Vol. 2015-October Association for Computing Machinery, 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Orrú, M, Tempero, E, Marchesi, M, Tonelli, R & Destefanis, G 2015, A curated benchmark collection of python systems for empirical studies on software engineering. in 11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015. vol. 2015-October, Association for Computing Machinery, 11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015, Beijing, China, 21/10/15. https://doi.org/10.1145/2810146.2810148

APA

Orrú, M., Tempero, E., Marchesi, M., Tonelli, R., & Destefanis, G. (2015). A curated benchmark collection of python systems for empirical studies on software engineering. In 11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015 (Vol. 2015-October). Association for Computing Machinery. https://doi.org/10.1145/2810146.2810148

Vancouver

Orrú M, Tempero E, Marchesi M, Tonelli R, Destefanis G. A curated benchmark collection of python systems for empirical studies on software engineering. In 11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015. Vol. 2015-October. Association for Computing Machinery. 2015 https://doi.org/10.1145/2810146.2810148

Author

Orrú, Matteo ; Tempero, Ewan ; Marchesi, Michele ; Tonelli, Roberto ; Destefanis, Giuseppe. / A curated benchmark collection of python systems for empirical studies on software engineering. 11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015. Vol. 2015-October Association for Computing Machinery, 2015.

Bibtex

@inproceedings{94ccdf902018450ba93d4d2a4abeca6a,
title = "A curated benchmark collection of python systems for empirical studies on software engineering",
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.",
keywords = "Curated Code Collection, Empirical Studies, Python",
author = "Matteo Orr{\'u} and Ewan Tempero and Michele Marchesi and Roberto Tonelli and Giuseppe Destefanis",
year = "2015",
month = "10",
day = "21",
doi = "10.1145/2810146.2810148",
language = "English",
volume = "2015-October",
booktitle = "11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015",
publisher = "Association for Computing Machinery",

}

RIS

TY - GEN

T1 - A curated benchmark collection of python systems for empirical studies on software engineering

AU - Orrú, Matteo

AU - Tempero, Ewan

AU - Marchesi, Michele

AU - Tonelli, Roberto

AU - Destefanis, Giuseppe

PY - 2015/10/21

Y1 - 2015/10/21

N2 - 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.

AB - 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.

KW - Curated Code Collection

KW - Empirical Studies

KW - Python

UR - http://www.scopus.com/inward/record.url?scp=84947610304&partnerID=8YFLogxK

U2 - 10.1145/2810146.2810148

DO - 10.1145/2810146.2810148

M3 - Conference contribution

VL - 2015-October

BT - 11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015

PB - Association for Computing Machinery

ER -