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Clustering Mobile Apps Based on Mined Textual Features

Al-Subaihin, AA; Sarro, F; Black, S; Capra, L; Harman, M; Jia, Y; Zhang, Y; (2016) Clustering Mobile Apps Based on Mined Textual Features. In: ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. ACM (Association for Computing Machinery) Green open access

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Abstract

CONTEXT: Categorising software systems according to their functionality yields many benefits to both users and developers. GOAL: 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 find a strong Spearman rank correlation (ρ= 0.96 for Google and ρ= 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 classification 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.

Type: Proceedings paper
Title: Clustering Mobile Apps Based on Mined Textual Features
Event: ESEM2016 - 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement
Location: Ciudad Real, Spain
Dates: 08 September 2016 - 09 September 2016
ISBN-13: 9781450344272
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/2961111.2962600
Publisher version: http://dl.acm.org/citation.cfm?id=2962600
Language: English
Additional information: © ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (2016). http://doi.acm.org/10.1145/2961111.2962600
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1538221
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