City Research Online

Abstract

Context: App stores provide a software development space and a market place that are both different from those to which we have become accustomed for traditional software development: The granularity is finer and there is a far greater source of information available for research and analysis. Information is available on price, customer rating and, through the data mining approach presented in this paper, the features claimed by app developers. These attributes make app stores ideal for empirical software engineering analysis.

Objective: This paper1 exploits App Store Analysis to understand the rich interplay between app customers and their developers.

Method: We use data mining to extract app descriptions, price, rating, and popularity information from the Blackberry World App Store, and natural language processing to elicit each apps’ claimed features from its description.

Results: The findings reveal that there are strong correlations between customer rating and popularity (rank of app downloads). We found evidence for a mild correlation between app price and the number of features claimed for the app and also found that higher priced features tended to be lower rated by their users. We also found that free apps have significantly (p-value < 0.001) higher ratings than non-free apps, with a moderately high effect size (Â12=0.68). All data from our experiments and analysis are made available on-line to support further investigations.

Publication Type:	Article
Additional Information:	© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Publisher Keywords:	App store analysis,App features, Mobile apps, Data mining, Natural language processing
Subjects:	H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Presidents's Portfolio
SWORD Depositor:	Symplectic Administrator

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