Quality In-Use of Mobile Geographic Information Systems for Data Collection

Abstract

Mobile Geographic Information Systems (GIS) plays a vital role in data collection, offering diverse functionalities for spatial data handling. Despite advancements, accurately determining the usage environment during development remains challenging. This study uses machine learning and natural language processing to automatically classify user reviews based on the ISO 25010 quality-in-use model. Motivated by the challenge of gauging user experience during development, stakeholders analyze user reviews for insights. An experimental study compares Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naive Bayes classifiers, revealing superior performance by SVM and Random Forest, particularly in efficiency evaluation. Findings underscore the efficacy of SVM in classifying user reviews, emphasizing its effectiveness in evaluating efficiency within mobile GIS applications. Moreover, it provides valuable insights for stakeholders, contributing to the enhancement of software quality of mobile GIS apps.