Researchers have shown that related functions can be mined from groupings of functions found in the version history of a system. Our first contribution is to expand this approach to a community of applications and set of similar applications. Android developers use a set of application programming interface (API) calls when creating apps. These API calls are used in similar ways across multiple applications. By clustering co-changing API calls used by 230 Android apps across 12k versions, we are able to predict the API calls that individual app developers will use with an average precision of 75% and recall of 22%. When we make predictions from the same category of app, such as Finance, we attain precision and recall of 81% and 28%, respectively. Our second contribution can be characterized as “programmers who discussed these functions were also interested in these functions.” Informal discussions on Stack Overflow provide a rich source of information about related API calls as developers provide solutions to common problems. By grouping API calls contained in each positively voted answer posts, we are able to create rules that predict the calls that app developers will use in their own apps with an average precision of 66% and recall of 13%. For comparison purposes, we developed a baseline by clustering co-changing API calls for each individual app and generated association rules from them. The baseline predicts API calls used by app developers with a precision and recall of 36% and 23%, respectively.

Gauging the success of software systems has been difficult in the past as there was no uniform measure. With mobile Application (App) Stores, users rate each App according to a common rating scheme. In this paper, we study the impact of App churn on the App success through the analysis of 154 free Android Apps that have a total of 1.2k releases. We provide a novel technique to extract Android API elements used by Apps that developers change between releases. We find that high App churn leads to lower user ratings. For example, we find that on average, per release, poorly rated Apps change 140 methods compared to the 82 methods changed by positively rated Apps. Our findings suggest that developers should not release new features at the expense of churn and user ratings. We also investigate the link between how frequently API classes and methods are changed by App developers relative to the amount of discussion of these code elements on StackOverflow. Our findings indicate that classes and methods that are changed frequently by App developers are in more posts on StackOverflow. We add to the growing consensus that StackOverflow keeps up with the documentation needs of practitioners.