The Effect of Green Software: A Study of Impact Factors on the Correctness of Software

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Title: The Effect of Green Software: A Study of Impact Factors on the Correctness of Software
Authors: Gil, David | Fernández-Alemán, Jose Luis | Trujillo Mondéjar, Juan Carlos | García-Mateos, Ginés | Luján-Mora, Sergio | Toval, Ambrosio
Research Group/s: Lucentia | Advanced deveLopment and empIrical research on Software (ALISoft)
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Data mining | E-learning | Experience report | Online judging | Logistic regression | Decision trees | Artificial neural network | Support vector machines | MOOC (massive open online courses)
Knowledge Area: Arquitectura y Tecnología de Computadores | Lenguajes y Sistemas Informáticos
Issue Date: 28-Sep-2018
Publisher: MDPI
Citation: Gil D, Fernández-Alemán JL, Trujillo J, García-Mateos G, Luján-Mora S, Toval A. The Effect of Green Software: A Study of Impact Factors on the Correctness of Software. Sustainability. 2018; 10(10):3471. doi:10.3390/su10103471
Abstract: Unfortunately, sustainability is an issue very poorly used when developing software and hardware systems. Lately, and in order to contribute to the earth sustainability, a new concept emerged named Green software which is computer software that can be developed and used efficiently and effectively with minimal or no impact to the environment. Currently, new teaching methods based on students’ learning process are being developed in the European Higher Education Area. Most of them are oriented to promote students’ interest in the course’s contents and offer personalized feedback. Online judging is a promising method for encouraging students’ participation in the e-learning process, although it still has to be researched and developed to be widely used and in a more efficient way. The great amount of data available in an online judging tool provides the possibility of exploring some of the most indicative attributes (e.g., running time, memory) for learning programming concepts, techniques and languages. So far, the most applied methods for automatically gathering information from the judging systems are based on statistical methods and, although providing reasonable correlations, these methods have not been proven to provide enough information for predicting grades when dealing with a huge amount of data. Therefore, the great novelty of this paper is to develop a data mining approach to predict program correctness as well as the grades of the students’ practices. For this purpose, powerful data mining technologies taken from the artificial intelligence domain have been used. In particular, in this study, we have used logistic regression, decision trees, artificial neural network and support vector machines; which have been properly identified as the most suitable ones for predicting activities in the e-learning domains. The results have achieved an accuracy of around 74%, both in the prediction of the program correctness as well as in the practice grades’ prediction. Another relevant issue provided in this paper is a comparison among these four techniques to obtain the best accuracy in predicting grades based on the availability of data as well as their taxonomy. The Decision Trees classifier has obtained the best confusion matrix, and time and memory efficiency were identified as the most important predictor variables. In view of these results, we can conclude that the development of green software leads programmers to implement correct software.
Sponsor: This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted project SEQUOIA-UA (TIN2015-63502-C3-3-R), project GINSENG-UMU (TIN2015-70259-C2-2-R) supported by the Spanish Ministry of Economy, Industry and Competitiveness and European FEDER funds. This work has also been partially funded by University of Alicante, under project GRE14-10 and by the Generalitat Valenciana, Spain, under project GV/2016/087.
ISSN: 2071-1050
DOI: 10.3390/su10103471
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Peer Review: si
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Appears in Collections:INV - LUCENTIA - Artículos de Revistas
INV - ALISoft - Artículos de Revistas

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