scholarly journals Evaluating Maintainability Prejudices with a Large-Scale Study of Open-Source Projects

2018 ◽  
pp. 151-171
Author(s):  
Tobias Roehm ◽  
Daniel Veihelmann ◽  
Stefan Wagner ◽  
Elmar Juergens
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Large Scale Study

A large-scale study of architectural evolution in open-source software systems

2016 ◽  
Vol 22 (3) ◽  
pp. 1146-1193 ◽  
Author(s):  
Pooyan Behnamghader ◽  
Duc Minh Le ◽  
Joshua Garcia ◽  
Daniel Link ◽  
Arman Shahbazian ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Software Systems ◽  
Large Scale Study

scholarly journals Gender differences and bias in open source: pull request acceptance of women versus men

2017 ◽  
Vol 3 ◽  
pp. e111 ◽  
Author(s):  
Josh Terrell ◽  
Andrew Kofink ◽  
Justin Middleton ◽  
Clarissa Rainear ◽  
Emerson Murphy-Hill ◽  
...  
Keyword(s):  
Gender Differences ◽  
Open Source ◽  
Gender Bias ◽  
Open Source Software ◽  
Large Scale ◽  
Women In The Workplace ◽  
Large Scale Study ◽  
Pull Request ◽  
Acceptance Rates

Biases against women in the workplace have been documented in a variety of studies. This paper presents a large scale study on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women’s contributions tend to be accepted more often than men’s. However, for contributors who are outsiders to a project and their gender is identifiable, men’s acceptance rates are higher. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

scholarly journals Code Coverage and Postrelease Defects: A Large-Scale Study on Open Source Projects

2017 ◽  
Vol 66 (4) ◽  
pp. 1213-1228 ◽  
Author(s):  
Pavneet Singh Kochhar ◽  
David Lo ◽  
Julia Lawall ◽  
Nachiappan Nagappan
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Code Coverage ◽  
Large Scale Study

scholarly journals A large-scale study of MPI usage in open-source HPC applications

2019 ◽  
Author(s):  
Ignacio Laguna ◽  
Ryan Marshall ◽  
Kathryn Mohror ◽  
Martin Ruefenacht ◽  
Anthony Skjellum ◽  
...  
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Large Scale Study

Exploring Temporal Reduction in Dialectal Spanish: A Large-scale Study of Lenition of Voiced Stops and Coda-s

2018 ◽  
Author(s):  
Ioana Vasilescu ◽  
Nidia Hernandez ◽  
Bianca Vieru ◽  
Lori Lamel
Keyword(s):  
Large Scale ◽  
Large Scale Study

scholarly journals Large-scale study of Toxoplasma and Cytomegalovirus shows an association between infection and serious psychiatric disorders

2019 ◽  
Vol 79 ◽  
pp. 152-158 ◽  
Author(s):  
Kristoffer Sølvsten Burgdorf ◽  
Betina B. Trabjerg ◽  
Marianne Giørtz Pedersen ◽  
Janna Nissen ◽  
Karina Banasik ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Large Scale ◽  
Large Scale Study

scholarly journals A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code

SLEEP ◽  
2020 ◽  
Author(s):  
Luca Menghini ◽  
Nicola Cellini ◽  
Aimee Goldstone ◽  
Fiona C Baker ◽  
Massimiliano de Zambotti
Keyword(s):  
Open Source ◽  
Validation Studies ◽  
Large Scale ◽  
Analytical Framework ◽  
Clinical Settings ◽  
Large Scale Data ◽  
Fast Pace ◽  
Epoch Analysis ◽  
Tracking Devices

Abstract Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland–Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.

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