Automatic Classification of Apps Reviews for Requirement Engineering: Exploring the Customers Need from Healthcare Applications

• Published in: 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) pp. 541 - 548
• Main Authors: Al Kilani, Nadeem, Tailakh, Rami, Hanani, Abualsoud
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: Computer bugs; Data Annotation; Feature extraction; Machine learning algorithms; Medical services; Mobile applications; Requirements Engineering; Security; Software; Supervised Machine Learning; Text Classification; User's Reviews
• DOI: 10.1109/SNAMS.2019.8931820

In one year, more than 6.5 million mobile applications have been listed for download on the application stores. That is, they are used by millions (or billions) of users across the world. Users express their daily experience of applications as reviews on those stores. This experience may include reporting bugs, demanding new features, posting feedback with regards to performance, reporting security issues, demanding user interface enhancements, and other needs. Interestingly, reviews could contain valuable information for the interest of application vendors and developers. However, the volume of such data is as huge, that is, traditional searching algorithms may not be efficient in extracting such useful information. Machine learning and data mining techniques are one of the popularly used algorithms to efficiently extracting significant information for Software Requirement Engineering; a key phase in the Software Engineering Life Cycle. In this paper, we experience machine learning algorithms and natural language processing techniques to classify a set of reviews about healthcare-domain applications into multiple types of categories such as bug reports, new feature requests, application performance, and user interface. For this purpose, we could extract more than 7500 reviews of ten different health-related mobile applications. More importantly, those reviews were annotated manually by software experts. In our experiments, we use the Weka tool employing different machine learning algorithms. We will also show what algorithms and features will perform better; in terms of accuracy using different evaluation metrics, when classifying reviews about mobile apps into various classes; bugs, new features, sentimental, general bug, usability, security, and performance. Moreover, the conducted experiments show that the overall performance improves when we use the data subset with highly confident labeling; when two experts agree on the same class. For the imbalanced-data problem, this research will show the effect of applying resampling techniques on improving classification accuracy as well.