Investigating the android apps' success: An empirical study

Measuring the success of software systems was not a trivial task in the past. Nowadays, mobile apps provide a uniform schema, i.e., the average ratings provided by the apps' users to gauge their success. While recent research has focused on examining the relationship between change-and fault-proneness and apps' lack of success, as well as qualitatively analyzing the reasons behind the apps' users dissatisfaction, there is little empirical evidence on the factors related to the success of mobile apps. In this paper, we explore the relationships between the mobile apps' success and a set of metrics that not only characterize the apps themselves but also the quality of the APIs used by the apps, as well as user attributes when they interact with the apps. In particular, we measure API quality in terms of bugs fixed in APIs used by apps and changes that occurred in the API methods. We examine different kinds of changes including changes in the interfaces, implementation, and exception handling. For user-related factors, we leverage the number of app's downloads and installations, and users' reviews. Through an empirical study of 474 free Android apps, we find that factors such as the number of users' reviews provided for an app, app's category and size appear to have an impact on the app's success

Software analytics: Challenges and opportunities

Nowadays, software development projects produce a large number of software artifacts including source code, execution traces, end-user feedback, as well as informal documentation such as developers' discussions, change logs, Stack-Overflow, and code reviews. Such data embeds rich and significant knowledge about software projects, their quality and services, as well as the dynamics of software development. Most often, this data is not organized, stored, and presented in a way that is immediately useful to software developers and project managers to support their decisions. To help developers and managers understand their projects, how they evolve, as well as support them during their decision-making process, software analytics - use of analysis, data, and systematic reasoning for making decisions - has become an emerging field of modern data analysis. While results obtained from analytics-based solutions suggested so far are promising, there are still several challenges associated with the adoption of software analytics into software development processes, as well as the development and integration of analytics tools in practical settings. We therefore propose a tutorial on software analytics. The tutorial will start with an introduction of software analytics. Next, we will discuss the main challenges and opportunities associated with software analytics based on the examples from our own research. These examples will cover a range of topics leveraging software analytics. The topics include mobile apps quality, code review process and its quality, analytics for the software engineering Twitter space, as well as the use of analytics to solve scheduling problems in the cloud