Towards Resolving Software Quality-in-Use Measurement Challenges
It addresses the difficulty in measuring software quality from user perspectives for applications like e-learning and mobile services, but is incremental as it builds on ISO standards and existing models.
This paper tackles the challenge of quantitatively measuring software quality-in-use by identifying issues in existing models and proposing a novel framework using sentiment analysis, with preliminary results showing prediction capabilities.
Software quality-in-use comprehends the quality from user's perspectives. It has gained its importance in e-learning applications, mobile service based applications and project management tools. User's decisions on software acquisitions are often ad hoc or based on preference due to difficulty in quantitatively measure software quality-in-use. However, why quality-in-use measurement is difficult? Although there are many software quality models to our knowledge, no works surveys the challenges related to software quality-in-use measurement. This paper has two main contributions; 1) presents major issues and challenges in measuring software quality-in-use in the context of the ISO SQuaRE series and related software quality models, 2) Presents a novel framework that can be used to predict software quality-in-use, and 3) presents preliminary results of quality-in-use topic prediction. Concisely, the issues are related to the complexity of the current standard models and the limitations and incompleteness of the customized software quality models. The proposed framework employs sentiment analysis techniques to predict software quality-in-use.