期刊
IEEE ACCESS
卷 9, 期 -, 页码 44647-44670出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3054730
关键词
Measurement; Quality assessment; Product design; Market research; Software quality; ISO Standards; Object oriented modeling; Software quality; software product quality; metrics; systematic mapping
资金
- Molde University College~Specialized University in Logistics, Norway
The study aims to analyze the research areas and trends on software product quality metrics from the literature in the past decade, serving as a foundation for future studies contributing to development in this field.
In the current competitive world, producing quality products has become a prominent factor to succeed in business. In this respect, defining and following the software product quality metrics (SPQM) to detect the current quality situation and continuous improvement of systems have gained tremendous importance. Therefore, it is necessary to review the present studies in this area to allow for the analysis of the situation at hand, as well as to enable us to make predictions regarding the future research areas. The present research aims to analyze the active research areas and trends on this topic appearing in the literature during the last decade. A Systematic Mapping (SM) study was carried out on 70 articles and conference papers published between 2009 and 2019 on SPQM as indicated in their titles and abstract. The result is presented through graphics, explanations, and the mind mapping method. The outputs include the trend map between the years 2009 and 2019, knowledge about this area and measurement tools, issues determined to be open to development in this area, and conformity between conference papers, articles and internationally valid quality models. This study may serve as a foundation for future studies that aim to contribute to the development in this crucial field. Future SM studies might focus on this subject for measuring the quality of network performance and new technologies such as Artificial Intelligence (AI), Internet of things (IoT), Cloud of Things (CoT), Machine Learning, and Robotics.
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