Clustering Mobile Apps Based on Mined Textual Features
Afnan A. Al-Subaihin, Federica Sarro, Sue Black, Licia Capra, Mark Harman, Yue Jia, and Yuanyuan Zhang.
10th International Conference on Empirical Software Engineering and Measurement (ESEM’16) (2016).
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Abstract
Context: Categorising software systems according to their functionality yields many bene fits to both users and developers.
Objective: In order to uncover the latent clustering of mobile apps in app stores, we propose a novel technique that measures app similarity based on claimed behaviour.
Method: Features are extracted using information retrieval augmented with ontological analysis and used as attributes to characterise apps. These attributes are then used to cluster the apps using agglomerative hierarchical clustering. We empirically evaluate our approach on 17,877 apps mined from the BlackBerry and Google app stores in 2014.
Results: The results show that our approach dramatically improves the existing categorisation quality for both Blackberry (from 0.02 to 0.41 on average) and Google (from 0.03 to 0.21 on average) stores. We also nd a strong Spearman rank correlation ( = 0:96 for Google and = 0:99 for BlackBerry) between the number of apps and the ideal granularity within each category, indicating that ideal granularity increases with category size, as expected.
Conclusions: Current categorisation in the app stores studied do not exhibit a good classi cation quality in terms of the claimed feature space. However, a better quality can be achieved using a good feature extraction technique and a traditional clustering method.