GitHub is the largest source code repository in the world. It provides a git-based source code management platform and also many features inspired by social networks. For example, GitHub users can show appreciation to projects by adding stars to them. Therefore, the number of stars of a repository is a direct measure of its popularity. In this paper, we use multiple linear regressions to predict the number of stars of GitHub repositories. These predictions are useful both to repository owners and clients, who usually want to know how their projects are performing in a competitive open source development market. In a large-scale analysis, we show that the proposed models start to provide accurate predictions after being trained with the number of stars received in the last six months. Furthermore, specific models---generated using data from repositories that share the same growth trends---are recommended for repositories with slow growth and or for repositories with less stars. Finally, we evaluate the ability to predict not the number of stars of a repository but its rank among the GitHub repositories. We found a very strong correlation between predicted and real rankings (Spearman's rho greater than 0.95).
Given the enormity of the app market and the velocity with which new apps arrive, it is extremely challenging for apps to reach the intended audience, or any audience at all, in fact. An entire "app promotion" industry exists to help publishers achieve post-launch app success. This is done by understanding relationships between app success and a variety of app attributes (like category, price, etc.). In this paper, we study a dimension not addressed thus far - the timing of app launch. Specifically, we study a large data set to uncover relationships between app launch times and its subsequent commercial success, or lack thereof. A number of interesting findings are revealed in this study. Users are generally less price-sensitive around holiday seasons, especially around Christmas and New Year's, in stark contrast, they are extra price sensitive during weekends. Specifically, more expensive apps released on weekends tend to get a higher negative word-of-mouth (review valence) rating. In addition, our results indicate that apps released in the latter part of the week tend to fare better than do apps released earlier. Furthermore, Thursday is the optimal day to release an app when considering review sentiments. Finally, it does tend to get a higher number of reviews on weekends.  During the recent years, the market of mobile software applications (apps) has maintained an impressive upward trajectory. Many small and large software development companies invest considerable resources to target available opportunities. As of today, the markets for such devices feature over 850K+ apps for Android and 900K+ for iOS. Availability, cost, functionality, and usability are just some factors that determine the success or lack of success for a given app. Among the other factors, reliability is an important criteria: users easily get frustrated by repeated failures, crashes, and other bugs; hence, abandoning some apps in favor of others. This paper reports a study analyzing how the fault- and change-proneness of APIs used by 7,097 (free) Android apps relates to applications' lack of success, estimated from user ratings. Results of this study provide important insights into a crucial issue: making heavy use of fault- and change-prone APIs can negatively impact the success of these apps. User review is a crucial component of open mobile app markets such as the Google Play Store. How do we automatically summarize millions of user reviews and make sense out of them? Unfortunately, beyond simple summaries such as histograms of user ratings, there are few analytic tools that can provide insights into user reviews. In this paper, we propose Wiscom, a system that can analyze tens of millions user ratings and comments in mobile app markets at three different levels of detail. Our system is able to (a) discover inconsistencies in reviews; (b) identify reasons why users like or dislike a given app, and provide an interactive, zoomable view of how users' reviews evolve over time; and (c) provide valuable insights into the entire app market, identifying users' major concerns and preferences of different types of apps. Results using our techniques are reported on a 32GB dataset consisting of over 13 million user reviews of 171,493 Android apps in the Google Play Store. We discuss how the techniques presented herein can be deployed to help a mobile app market operator such as Google as well as individual app developers and end-users.     The tremendous rate of growth in the mobile app market over the past few years has attracted many developers to build mobile apps. However, while there is no shortage of stories of how lone developers have made great fortunes from their apps, the majority of developers are struggling to break even. For those struggling developers, knowing the “DNA” (i.e., characteristics) of high-rated apps is the first step towards successful development and evolution of their apps. In this paper, we investigate 28 factors along eight dimensions to understand how high-rated apps are different from low-rated apps. We also investigate what are the most influential factors by applying a random-forest classifier to identify high-rated apps. Through a case study on 1,492 high-rated and low-rated free apps mined from the Google Play store, we find that high-rated apps are statistically significantly different in 17 out of the 28 factors that we considered. Our experiment also shows that the size of an app, the number of promotional images that the app displays on its web store page, and the target SDK version of an app are the most influential factors. One of the most popular ways to monetize a free app is by including advertisements in the app. Several advertising (ad) companies provide these ads to app developers through ad libraries that need to be integrated in the app. However, the demand for ads far exceeds the supply. This obstacle may lead app developers to integrate several ad libraries from different ad companies in their app to ensure they receive an ad with each request. However, no study has explored how many ad libraries are commonly integrated into apps. Additionally, no research to date has examined whether integrating many different ad libraries impacts an app's ratings. This article examines these two issues by empirically examining thousands of Android apps. The authors find that there are apps with as many as 28 ad libraries, but they find no evidence that the number of ad libraries in an app is related to its possible rating in the app store. However, integrating certain ad libraries can negatively impact an app's rating.  