Cyberinfrastructures: Bridging the Divide between Scientific Research and Software Engineering

Computer ◽  
2014 ◽  
Vol 47 (8) ◽  
pp. 48-55 ◽  
Author(s):  
Ian Gorton
Keyword(s):  
Software Engineering ◽  
Scientific Research

scholarly journals An Expert Judgment in Source Code Quality Research Domain—A Comparative Study between Professionals and Students

2020 ◽  
Vol 10 (20) ◽  
pp. 7088
Author(s):  
Luka Pavlič ◽  
Marjan Heričko ◽  
Tina Beranič
Keyword(s):  
Experimental Data ◽  
Software Engineering ◽  
Research Evidence ◽  
Source Code ◽  
Scientific Research ◽  
Controlled Experiments ◽  
Quality Research ◽  
Professional Responses ◽  
Code Quality ◽  
Research Domain

In scientific research, evidence is often based on empirical data. Scholars tend to rely on students as participants in experiments in order to validate their thesis. They are an obvious choice when it comes to scientific research: They are usually willing to participate and are often themselves pursuing an education in the experiment’s domain. The software engineering domain is no exception. However, readers, authors, and reviewers do sometimes question the validity of experimental data that is gathered in controlled experiments from students. This is why we will address this difficult-to-answer question: Are students a proper substitute for experienced professional engineers while performing experiments in a typical software engineering experiment. As we demonstrate in this paper, it is not a “yes or no” answer. In some aspects, students were not outperformed by professionals, but in others, students would not only give different answers compared to professionals, but their answers would also diverge. In this paper we will show and analyze the results of a controlled experiment in the source code quality domain in terms of comparing student and professional responses. We will show that authors have to be careful when employing students in experiments, especially when complex and advanced domains are addressed. However, they may be a proper substitution in cases, where non-advanced aspects are required.

scholarly journals Leveraging Containers for Reproducible Psychological Research

2021 ◽  
Author(s):  
Kristina Wiebels ◽  
David Moreau
Keyword(s):  
Software Engineering ◽  
Operating Systems ◽  
Large Scale ◽  
Scientific Research ◽  
Psychological Research ◽  
Open Science ◽  
Research Project ◽  
Worked Example ◽  
Crucial Component ◽  
Set Up

Containers have become increasingly popular in computing and software engineering, and are gaining traction in scientific research. They allow packaging up all code and dependencies to ensure that analyses run reliably across a range of operating systems and software versions. Despite being a crucial component for reproducible science, containerization has yet to become mainstream in psychology. In this tutorial, we describe the logic behind containers, what they are, and the practical problems they can solve. We walk the reader through the implementation of containerization within a research workflow, with examples using Docker and R. Specifically, we describe how to use existing containers, build personalized containers, and share containers alongside publications. We provide a worked example that includes all steps required to set up a container for a research project and can easily be adapted and extended. We conclude with a discussion of the possibilities afforded by the large-scale adoption of containerization, especially in the context of cumulative, open science, toward a more efficient and inclusive research ecosystem.

Scientific research ontology to support systematic review in software engineering

2007 ◽  
Vol 21 (2) ◽  
pp. 133-151 ◽  
Author(s):  
Jorge Calmon de Almeida Biolchini ◽  
Paula Gomes Mian ◽  
Ana Candida Cruz Natali ◽  
Tayana Uchôa Conte ◽  
Guilherme Horta Travassos
Keyword(s):  
Systematic Review ◽  
Software Engineering ◽  
Scientific Research ◽  
Research Ontology

scholarly journals Leveraging Containers for Reproducible Psychological Research

2021 ◽  
Vol 4 (2) ◽  
pp. 251524592110178
Author(s):  
Kristina Wiebels ◽  
David Moreau
Keyword(s):  
Software Engineering ◽  
Operating Systems ◽  
Large Scale ◽  
Scientific Research ◽  
Psychological Research ◽  
Open Science ◽  
Research Project ◽  
Worked Example ◽  
Crucial Component ◽  
Set Up

Containers have become increasingly popular in computing and software engineering and are gaining traction in scientific research. They allow packaging up all code and dependencies to ensure that analyses run reliably across a range of operating systems and software versions. Despite being a crucial component for reproducible science, containerization has yet to become mainstream in psychology. In this tutorial, we describe the logic behind containers, what they are, and the practical problems they can solve. We walk the reader through the implementation of containerization within a research workflow with examples using Docker and R. Specifically, we describe how to use existing containers, build personalized containers, and share containers alongside publications. We provide a worked example that includes all steps required to set up a container for a research project and can easily be adapted and extended. We conclude with a discussion of the possibilities afforded by the large-scale adoption of containerization, especially in the context of cumulative, open science, toward a more efficient and inclusive research ecosystem.

scholarly journals How do Brazilian Software Engineering Researchers Perceive and Practice Open Science?

2021 ◽  
Author(s):  
Edson OliveiraJr ◽  
Christina von Flach G. Chavez ◽  
André F. R. Cordeiro ◽  
Daniela Feitosa
Keyword(s):  
Software Engineering ◽  
Scientific Research ◽  
Open Science ◽  
Empirical Software Engineering ◽  
Engineering Community ◽  
Research Areas ◽  
Engineering Journal ◽  
Science Initiative ◽  
Science Policies

With the wide popularization and increasing adoption of Open Science, most scientific research areas have discussed its benefits to the overall society represented by any citizen. The openness process aims at promoting free availability of such researches, thus directly impacting scientific evolution. Researchers are encouraged to make scientific research artifacts open for every citizen. In the Software Engineering area we are currently experiencing international Open Science initiatives, such as the ICSE Rose Festival, the ESEM Open Science policies, and the Empirical Software Engineering journal Open Science initiative. However, a little is known about Open Science in the Brazilian Software Engineering community. Therefore, in this paper, we present and discuss the results of a survey on how do our software engineering community perceive and practice Open Science.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Abstraction: An alternative neurocognitive account of recognition, prediction, and decision making

2020 ◽  
Vol 43 ◽  
Author(s):  
Valerie F. Reyna ◽  
David A. Broniatowski
Keyword(s):  
Decision Making ◽  
Software Engineering ◽  
Systems Engineering ◽  
Evidence Based ◽  
Extensive Literature ◽  
Trace Theory ◽  
Fuzzy Trace Theory ◽  
Abstract Representations ◽  
Technical Systems

Abstract Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.

Social-Scientific Research Competency

2019 ◽  
Vol 35 (5) ◽  
pp. 737-750 ◽  
Author(s):  
Christopher Gess ◽  
Christoph Geiger ◽  
Matthias Ziegler
Keyword(s):  
Higher Education ◽  
Test Scores ◽  
Research Performance ◽  
Scientific Research ◽  
Research Education ◽  
Crystallized Intelligence ◽  
Research Competency ◽  
Social Scientific Research ◽  
Social Scientific ◽  
Education Courses

Abstract. Although the development of research competency is an important goal of higher education in social sciences, instruments to measure this outcome often depend on the students’ self-ratings. To provide empirical evidence for the utility of a newly developed instrument for the objective measurement of social-scientific research competency, two validation studies across two independent samples were conducted. Study 1 ( n = 675) provided evidence for unidimensionality, expected differences in test scores between differently advanced groups of students as well as incremental validities over and above self-perceived research self-efficacy. In Study 2 ( n = 82) it was demonstrated that the competency measured indeed is social-scientific and relations to facets of fluid and crystallized intelligence were analyzed. Overall, the results indicate that the test scores reflected a trainable, social-scientific, knowledge-related construct relevant to research performance. These are promising results for the application of the instrument in the evaluation of research education courses in higher education.

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