Quality assurance automation is essential in modern software development. In practice, this automation is supported by a multitude of tools that fit different needs and require developers to make decisions about which tool to choose in a given context. Data and analytics of the pros and cons can inform these decisions. Yet, in most cases, there is a dearth of empirical evidence on the effectiveness of existing practices and tool choices. We propose a general methodology to model the time-dependent effect of automation tool choice on four outcomes of interest: prevalence of issues, code churn, number of pull requests, and number of contributors, all with a multitude of controls. On a large data set of npm JavaScript projects, we extract the adoption events for popular tools in three task classes: linters, dependency managers, and coverage reporters. Using mixed methods approaches, we study the reasons for the adoptions and compare the adoption effects within each class, and sequential tool adoptions across classes. We find that some tools within each group are associated with more beneficial outcomes than others, providing an empirical perspective for the benefits of each. We also find that the order in which some tools are implemented is associated with varying outcomes.

The incredible success of the mobile App economy has been attracting software developers hoping for new or repeated success. Surviving in the fierce competitive App market involves in part planning ahead of time for the success of the App on the marketplace. Prior research has shown that App success can be viewed through its proxy-popularity. An important question, then, is what factors differentiates popular from unpopular Apps? GitHub, a software project forge, and Google Play store, an app market, are both crowdsourced, and provide some publicly available data that can be used to cross-link source code and app download popularity. In this study, we examined how technical and social features of Open Source Software Apps, mined from two crowdsourced websites, relate to App popularity. We observed that both the technical and the social factors play significant roles in explaining App popularity. However, the combined factors have a low effect size in explaining App popularity, as measured by average user rating on Google Play. Interestingly on GitHub, we found that social factors have a higher power in explaining the popularity compared to all the technical factors we investigated.

Modern software development is increasingly collaborative. Open Source Software (OSS) are the bellwether; they support dynamic teams, with tools for code sharing, communication, and issue tracking. The success of an OSS project is reliant on team communication. E.g., in issue discussions, individuals rely on rhetoric to argue their position, but also maintain technical relevancy. Rhetoric and technical language are on opposite ends of a language complexity spectrum: the former is stylistically natural; the latter is terse and concise. Issue discussions embody this duality, as developers use rhetoric to describe technical issues. The style mix in any discussion can define group culture and affect performance, e.g., issue resolution times may be longer if discussion is imprecise. Using GitHub, we studied issue discussions to understand whether project-specific language differences exist, and to what extent users conform to a language norm. We built project-specific and overall GitHub language models to study the effect of perceived language complexity on multiple responses. We find that experienced users conform to project-specific language norms, popular individuals use overall GitHub language rather than project-specific language, and conformance to project-specific language norms reduces issue resolution times. We also provide a tool to calculate project-specific perceived language complexity.