A novel framework for measuring software quality-in-use based on semantic similarity and sentiment analysis of software reviews

Software quality in use (QinU) relates to human-software interactions when a software product is used in a particular context. Currently, QinU measurement models are bound to ineffective measurement formulation and many models are subjectively incoherent. This paper proposes a novel QinU framework (QinUF) to measure QinU competently consuming software reviews. The framework has three components: QinU prediction, polarity classification, and QinU scoring. The QinU prediction component computationally maps software review-sentences to its respective QinU characteristics (topics) of the ISO 25010 model based on a text similarity measure. The topic prediction problem is run as a text to text similarity; where the first text (test) is the actual unlabeled review-sentence and the second text is the set of selected features (keywords) from a benchmark dataset. The polarity classification component classifies each test sentence to its polarity orientation; the respective sentimental values are recorded. To score QinU, the sentimental values are grouped and summarized into their respective QinU topics. The QinUF evaluation over real-life scenarios showed that the QinUF automates software QinU measurement; therefore, users could compare and acquire software on the fly. The framework is consistent and superior to related compared works.