Sergio Salomón, Rafael Duque, Santos Bringas, Káthia Marçal de Oliveira
{"title":"Quality-in-Use in Practice: A Study for Context-Aware Software Systems in Pervasive Environments","authors":"Sergio Salomón, Rafael Duque, Santos Bringas, Káthia Marçal de Oliveira","doi":"10.1002/smr.2764","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Software quality models have increasingly emphasized human factors and user needs. In 2011, ISO/IEC 25010 introduced the quality-in-use (QinU) model, designed to evaluate software quality as an outcome of a user utilizing a system through the evaluation of five characteristics: effectiveness, efficiency, freedom from risk, satisfaction, and context coverage. As a generic reference, this standard has been customized for various software types (e.g., web portals and artificial intelligence systems). This article presents a customization for context-aware software systems (CASSs), which are software systems that interpret and use context information (regarding the user, the software application features, and the environment) to adapt their functionalities. We are particularly interested in CASS for pervasive, or ubiquitous, environments. To address this goal, each QinU characteristic was analyzed by professionals from the academy and industry, taking into account the CASS features for pervasive/ubiquitous environments. A cyclical process of definition, revision, and improvement based on measurement theory was carried out before empirical validation in case studies. As the main result, a novel set of QinU measures specifically tailored for CASSs in a pervasive environment is provided, considering not only the classic explicit user interactions (e.g., mouse clicks and text input) but also the implicit interactions during everyday activities (e.g., walking or driving), captured through sensors and processed to support the user (e.g., recommending nearby museums and providing driving guidance). This set of measures supports CASS assessment and improvements, offering more accurate and context-sensitive quality measurement.</p>\n </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Software-Evolution and Process","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/smr.2764","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Software quality models have increasingly emphasized human factors and user needs. In 2011, ISO/IEC 25010 introduced the quality-in-use (QinU) model, designed to evaluate software quality as an outcome of a user utilizing a system through the evaluation of five characteristics: effectiveness, efficiency, freedom from risk, satisfaction, and context coverage. As a generic reference, this standard has been customized for various software types (e.g., web portals and artificial intelligence systems). This article presents a customization for context-aware software systems (CASSs), which are software systems that interpret and use context information (regarding the user, the software application features, and the environment) to adapt their functionalities. We are particularly interested in CASS for pervasive, or ubiquitous, environments. To address this goal, each QinU characteristic was analyzed by professionals from the academy and industry, taking into account the CASS features for pervasive/ubiquitous environments. A cyclical process of definition, revision, and improvement based on measurement theory was carried out before empirical validation in case studies. As the main result, a novel set of QinU measures specifically tailored for CASSs in a pervasive environment is provided, considering not only the classic explicit user interactions (e.g., mouse clicks and text input) but also the implicit interactions during everyday activities (e.g., walking or driving), captured through sensors and processed to support the user (e.g., recommending nearby museums and providing driving guidance). This set of measures supports CASS assessment and improvements, offering more accurate and context-sensitive quality measurement.