GDL

Journal from gdlhub / 2020-04-07 10:39:09
Oleh : Issa Atoum, King Saud University
Dibuat : 2020-01-06, dengan 1 file

Keyword : ISO25010, Quality in use, Sentiment analysis, Software quality, Text similarity
Url : http://www.sciencedirect.com/science/article/pii/S1319157817305268
Sumber pengambilan dokumen : Web

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.

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