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

Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

pDroid

When an end user attempts to download an app on the Google Play Store they receive two related items that can be used to assess the potential threats of an application, the list of permissions used by the application and the textual description of the application. However, this raises several concerns. First, applications tend to use more permissions than they need and end users are not tech-savvy enough to fully understand the security risks. Therefore, it is challenging to assess the threats of an application fully by only seeing the permissions. On the other hand, most textual descriptions do not clearly define why they need a particular permission. These two issues conjoined make it difficult for end users to accurately assess the security threats of an application. This has lead to a demand for a framework that can accurately determine if a textual description adequately describes the actual behavior of an application. In this Master Thesis, we present pDroid (short for privateDroid), a market-independent framework that can compare an Android application’s textual description to its internal behavior. We evaluated pDroid using 1562 benign apps and 243 malware samples, and pDroid correctly classified 91.4% of malware with a false positive rate of 4.9%

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