Social metrics included in prediction models on software engineering: A mapping study

Context: Previous work that used prediction models on Software Engineering included few social metrics as predictors, even though many researchers argue that Software Engineering is a social activity. Even when social metrics were considered, they were classified as part of other dimensions, such as process, history, or change. Moreover, few papers report the individual effects of social metrics. Thus, it is not clear yet which social metrics are used in prediction models and what are the results of their use in different contexts. Objective: To identify, characterize, and classify social metrics included in prediction models reported in the literature. Method: We conducted a mapping study (MS) using a snowballing citation analysis. We built an initial seed list adapting strings of two previous systematic reviews on software prediction models. After that, we conducted backward and forward citation analysis using the initial seed list. Finally, we visited the profile of each distinct author identified in the previous steps and contacted each author that published more than 2 papers to ask for additional candidate studies. Results: We identified 48 primary studies and 51 social metrics. We organized the metrics into nine categories, which were divided into three groups-communication, project, and commit-related. We also mapped the applications of each group of metrics, indicating their positive or negative effects. Conclusions: This mapping may support researchers and practitioners to build their prediction models considering more social metrics