Incorporation of ISO 25010 with machine learning to develop a novel quality in use prediction system (QiUPS)

Osama Alshareet (), Awni Itradat (), Iyad Abu Doush () and Ahmad Quttoum ()
Additional contact information
Osama Alshareet: Hashemite University
Awni Itradat: Hashemite University
Iyad Abu Doush: Yarmouk University
Ahmad Quttoum: Hashemite University

International Journal of System Assurance Engineering and Management, 2018, vol. 9, issue 2, No 4, 344-353

Abstract: Abstract Guided by the eagerness to fulfill business objectives, quality assurance has become one of the highlighted topics in software engineering. With the rise of globalization and free markets, software users are becoming increasingly powerful with their ability to buy or reject computer software. While there is agreement over achieving quality, there is debate over the definition of quality. To illustrate, literature shows inconsistencies between a software development team definition to quality and a user definition to quality. Recently, there is a tendency amongst researchers to appreciate the need for studying quality from a user prospective. Following a systematic approach, this research attempts to develop a QiUPS, an expert system for predicting quality in use in early software development phases. With the scariness of research data in this field, the research generates a dataset from the documentation of Information, Communication, and E-learning Technology Centre software projects. The research methodology followed a comparative approach as it statistically compared four different classification algorithms (CAs) in terms of accuracy in classifying the research dataset. After that, the research results led the researchers to compare the performance of artificial neural networks with convolutional neural networks in three empirical experiments, which is rarely researched. Finally, the research incorporated the best CA with ISO 25010 in order to develop the novel QiUPS. The research results are consistent and contributive to this rarely researched area.

Keywords: Quality in use prediction system (QiUPS); ISO 25010 software quality model; Classification algorithms (CAs); Artificial neural networks (ANN); Convolutional neural networks (CNN); Quality in use (QiU); User-centered applications (UCA); Multi-layer perceptron (MLP) (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-017-0649-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:9:y:2018:i:2:d:10.1007_s13198-017-0649-x

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-017-0649-x

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().