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

Alshareet, Osama; Itradat, Awni; Doush, Iyad Abu; Quttoum, Ahmad Nahar

doi:10.1007/s13198-017-0649-x

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…are many other studies applying machine learning and data mining approaches for automation and knowledge discovery. These are mostly based on natural language processing and sentiment analysis of user reviews (Atoum 2020;Jiang et al 2019;Leopairote et al 2013;Qian et al 2016), but also pattern recognition and classification of metrics (Rana and Staron 2015) or neural networks for metric classification (Alshareet et al 2018). All process mining studies are considered as data mining approaches as well.…”

Section: Discussionmentioning

confidence: 99%

“…This framework does not consider context characteristics. Alshareet et al (2018) present an approach for QinU prediction using custom metrics extracted from projects documentation and neural networks to classify levels of QinU from these metrics. Many proposals based the QinU evaluation on users' reviews and opinion mining (Leopairote et al 2013;Qian et al 2016;Jiang et al 2019;Atoum 2020).…”

Section: Related Workmentioning

confidence: 99%

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Towards automatic evaluation of the Quality-in-Use in context-aware software systems

Salomón¹,

Duque

Montaña

et al. 2022

J Ambient Intell Human Comput

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Context-aware systems adapt their services to the user’s intentions and environment to improve the user experience. However, how to evaluate the quality of these systems in terms of user perception and context recognition is still an open problem. Our goal in this work is to evaluate the Quality-in-Use (QinU) for context-aware software systems according to the ISO/IEC 25010 standard and in an automated manner. This evaluation is oriented to be model-based, with domain specification and log data as input, while quality metrics and representations of users’ behavior as output. In this process, we use probabilistic models to discover user patterns, heuristic metrics as QinU estimation, clustering techniques to obtain user profiles according to their QinU, and feature selection to identify relevant factors of context. We propose a framework for assessing the QinU in context-aware software systems called Framework for Assessing Quality-in-use of Software (FAQuiS). FAQuiS includes a set of models to represent all dimensions of context, a methodology to apply the quality analysis to any system, and a set of tools and metrics to support and automate the process. We seek to test the impact and ease of integration in the industry for this framework. A case study in a company allows us to validate the applicability in a real environment. We analyze the mechanisms that support the QinU evaluation in context-aware systems, the feasibility of the QinU quantification, and the suitability of the integration in companies. Compared to previous works, our proposal offers a novel data-driven approach with general-purpose and industrial viability. FAQuiS can be used as a solution to assess the QinU based on the ISO 25010 standard and the models of user behaviors in different contexts. This solution analyzes the context changes in the user interaction, can quantify the quality loss in these contexts, and does not require big efforts to be integrated into a software development process.

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