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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 paper11This paper is an extended version of our short paper at MSR 2012 [1]; a technical report is also available [2]. 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 (A^12=0.68). All data from our experiments and analysis are made available on-line to support further investigations.
Highlights
App stores provide a rich source of information about apps concerning their customer, business, and technically- focused attributes
The feature information we extract reflects features that are present in the descriptions of apps, but they are not necessarily present in the app itself. We believe that this is an interesting aspect of our app store analysis: it gives us an opportunity to explore the relationship between claimed features and other app store data
We developed a simple four-step Natural Language Processing (NLP) algorithm to extract feature information and implemented it using the Natural Language Toolkit (NLTK), a comprehensive natural language processing package written in Python [10]
Summary
This paper 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
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