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 (A^12=0.68). All data from our experiments and analysis are made available on-line to support further investigations.

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges.

App developers would like to understand the impact of their own and their competitors’ software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings.

Many papers on App Store Mining are susceptible to the App Sampling Problem, which exists when only a subset of apps are studied, resulting in potential sampling bias. We introduce the App Sampling Problem, and study its effects on sets of user review data. We investigate the effects of sampling bias, and techniques for its amelioration in App Store Mining and Analysis, where sampling bias is often unavoidable. We mine 106,891 requests from 2,729,103 user reviews and investigate the properties of apps and reviews from 3 different partitions: the sets with fully complete review data, partially complete review data, and no review data at all. We find that app metrics such as price, rating, and download rank are significantly different between the three completeness levels. We show that correlation analysis can find trends in the data that prevail across the partitions, offering one possible approach to App Store Analysis in the presence of sampling bias.

We introduce a theoretical characterisation of feature lifecycles in app stores, to help app developers to identify trends and to find undiscovered requirements. To illustrate and motivate app feature lifecycle analysis, we use our theory to empirically analyse the migratory and non-migratory behaviours of 4,053 non-free features from two App Stores (Samsung and BlackBerry). The results reveal that, in both stores, intransitive features (those that neither migrate nor die out) exhibit significantly different behaviours with regard to important properties, such as their price. Further correlation analysis also highlights differences between trends relating price, rating, and popularity. Our results indicate that feature lifecycle analysis can yield insights that may also help developers to understand feature behaviours and attribute relationships.

Reports recent work on the impact of the European STAR programme on the process of economic regeneration in Northern Ireland. Seeks to draw lessons from the Northern Ireland experience which may be applied elsewhere. Reports responses among the business community to the STAR programme and in particular the impact of the fibre opticbased transmission and switching facilities it has provided. In considering the overall impact of STAR on the job market, comparisons are drawn with elsewhere in the UK. The main conclusion is that in promoting the virtues of advanced telecommunications services, much greater attention must be paid to the business benefits involved and also, to making facilities such as ISDN more user friendly and competitive.