A survey of app store analysis for software engineering

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

Gamification for health promotion: systematic review of behaviour change techniques in smartphone apps

Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical, Research Council and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged

Understanding the price drivers of successful apps in the mobile app market

In this paper, we take the perspective of app developers. Specifically, based on a sample of top paid apps from three major app stores, i.e., App Store, Google Play, and Blackberry World, we construct a hedonic price model to examine the role of relevant factors in price formation in the app market. Our results suggest a strong evidence of two-sided market effects. In fact, the lower price charged for apps operating as two-sided markets reflect the strategy of subsidising users, due to the positive cross-side externalities they exert on valuable third parties. Surprisingly, the effects of trialability, in-app purchase and mechanisms to build reputation are not significant in the context of successful apps. Finally, we find weak evidence that developers of top paid apps prefer price skimming to penetration price strategies

A First Look at Android Applications in Google Play related to Covid-19

Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the Covid-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a First taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the Covid-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.Comment: Accepted in Empirical Software Engineering under reference: EMSE-D-20-00211R

Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review

© 2013 IEEE. The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented

On the Importance of Performing App Analysis Within Peer Groups

The competing nature of the app market motivates us to shift our focus on apps that provide similar functionalities and directly compete with each other (i.e., peer apps). In this work, we study the ratings and the review text of 100 Android apps across 10 peer app groups. We highlight the importance of performing peer-app analysis by showing that it can provide a unique perspective over performing a global analysis of apps (i.e., mixing apps from multiple categories). First, we observe that comparing user ratings within peer groups can provide very different results from comparing user ratings from a global perspective. Then, we show that peer-app analysis provides a different perspective to spot the dominant topics in the user reviews, and to understand the impact of the topics on user ratings. Our findings suggest that future efforts may pay more attention to performing and supporting app analysis from a peer group context. For example, app store owners may consider an additional rating mechanism that normalizes app ratings within peer groups, and future research may help developers understand the characteristics of specific peer groups and prioritize their efforts

App Store Analysis for Software Engineering

App Store Analysis concerns the mining of data from apps, made possible through app stores. This thesis extracts publicly available data from app stores, in order to detect and analyse relationships between technical attributes, such as software features, and non-technical attributes, such as rating and popularity information. The thesis identifies the App Sampling Problem, its effects and a methodology to ameliorate the problem. The App Sampling Problem is a fundamental sampling issue concerned with mining app stores, caused by the rather limited ‘most-popular-only’ ranked app discovery present in mobile app stores. This thesis provides novel techniques for the analysis of technical and non-technical data from app stores. Topic modelling is used as a feature extraction technique, which is shown to produce the same results as n-gram feature extraction, that also enables linking technical features from app descriptions with those in user reviews. Causal impact analysis is applied to app store performance data, leading to the identification of properties of statistically significant releases, and developer-controlled properties which could increase a release’s chance for causal significance. This thesis introduces the Causal Impact Release Analysis tool, CIRA, for performing causal impact analysis on app store data, which makes the aforementioned research possible; combined with the earlier feature extraction technique, this enables the identification of the claimed software features that may have led to significant positive and negative changes after a release

The public health potential of mobile applications to increase physical activity

Background: Physical activity (PA) is an important behavioural determinant of morbidity and mortality and is a public health priority. The accessibility, convenience and wide reach of mobile applications (apps) makes these digital interventions a potential mode for delivering PA interventions at scale. At the end of 2017 there were 325,000 health apps available publicly, with “fitness” apps being the largest category of all health apps. However, most apps on the market have not been evaluated and little is known about their quality. Aim: This PhD investigated the public health potential of publicly available PA apps. Methods: The following studies were conducted: 1) a review and content analysis of the most popular PA apps on the market to assess their quality, defined as safety, likely efficacy and positive user experience; 2) a study using regression models to determine the association between popularity and quality of those apps; 3) a feasibility crossover trial assessing two apps for increasing PA; and 4) a qualitative study assessing the acceptability of the trial procedures and exploring the experiences of the two PA apps. Results: Popular apps had high usability but there were issues around their safety and likely efficacy. Popularity was not associated with likely efficacy. The feasibility trial and the qualitative study showed that such a trial would be feasible and acceptable to participants. The enablers and barriers to increasing exercise using the apps were identified. Conclusion: The discrepancy between quality and popularity represents a missed opportunity for behaviour change interventions. Hence, the public health impact of PA apps is unlikely to be achieved when market forces “prescribe” what is used by the public. The motivation to use the apps varied substantially and it is important to identify when, for whom, and in what context PA apps are most likely to facilitate behaviour change