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

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.