A framework to predict software "quality in use" from software reviews

Abstract

Software reviews are verified to be a good source of users' experience. The software "quality in use" concerns meeting users' needs. Current software quality models such as McCall and Boehm, are built to support software development process, rather than users perspectives. In this paper, opinion mining is used to extract and summarize software "quality in use" from software reviews. A framework to detect software "quality in use" as defined by the ISO/IEC 25010 standard is presented here. The framework employs opinion-feature double propagation to expand predefined lists of software "quality in use" features to domain specific features. Clustering is used to learn software feature "quality in use" characteristics group. A preliminary result of extracted software features shows promising results in this direction. © Springer Science+Business Media Singapore 2014.