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).
@cite_30 record time-series information about popular Google Play apps and investigate how release frequency can affect an app's performance, as measured by rating, popularity and number of user reviews. They label as impactful releases'' the ones that caused a significant change on the app's popularity, as inferred by Causal Impact Analysis (a form of causal inference). They report that more mentions of features and fewer mentions of bug fixing increase the chance for a release to be impactful. @cite_10 follow a similar approach but to identify causal relationships between changes in internal measures of software quality (coupling, cohesion, complexity, etc) and the number of defects reported for a system.
@cite_30 @cite_10
2546780486 2891288355 2104074028 2292487094
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. Developers always focus on delivering high-quality updates to improve, or maintain the rating of their apps. Prior work has studied user reviews by analyzing all reviews of an app. However, this app-level analysis misses the point that users post reviews to provide their feedback on a certain update. For example, two bad updates of an app with a history of good updates would not be spotted using app-level analysis. In this paper, we examine reviews at the update-level to better understand how users perceive bad updates. We focus our study on the top 250 bad updates (i.e., updates with the highest increase in the percentage of negative reviews relative to the prior updates of the app) from 26,726 updates of 2,526 top free-to-download apps in the Google Play Store. We find that feature removal and UI issues have the highest increase in the percentage of negative reviews. Bad updates with crashes and functional issues are the most likely to be fixed by a later update. However, developers often do not mention these fixes in the release notes. Our work demonstrates the necessity of an update-level analysis of reviews to capture the feelings of an app's user-base about a particular update. The paper presents an adaptive approach to perform impact analysis from a given change request to source code. Given a textual change request (e.g., a bug report), a single snapshot (release) of source code, indexed using Latent Semantic Indexing, is used to estimate the impact set. Should additional contextual information be available, the approach configures the best-fit combination to produce an improved impact set. Contextual information includes the execution trace and an initial source code entity verified for change. Combinations of information retrieval, dynamic analysis, and data mining of past source code commits are considered. The research hypothesis is that these combinations help counter the precision or recall deficit of individual techniques and improve the overall accuracy. The tandem operation of the three techniques sets it apart from other related solutions. Automation along with the effective utilization of two key sources of developer knowledge, which are often overlooked in impact analysis at the change request level, is achieved. To validate our approach, we conducted an empirical evaluation on four open source software systems. A benchmark consisting of a number of maintenance issues, such as feature requests and bug fixes, and their associated source code changes was established by manual examination of these systems and their change history. Our results indicate that there are combinations formed from the augmented developer contextual information that show statistically significant improvement over stand-alone approaches. Recently emerged app markets provide a centralized paradigm for software distribution in smartphones. The difficulty of massively collecting app data has led to a lack a good understanding of app market dynamics. In this paper we seek to address this problem, through a detailed temporal analysis of Google Play, Google's app market. We perform the analysis on data that we collected daily from 160,000 apps, over a period of six months in 2012. We report often surprising results. For instance, at most 50 of the apps are updated in all categories, which significantly impacts the median price. The average price does not exhibit seasonal monthly trends and a changing price does not show any observable correlation with the download count. In addition, productive developers are not creating many popular apps, but a few developers control apps which dominate the total number of downloads. We discuss the research implications of such analytics on improving developer and user experiences, and detecting emerging threat vectors.