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.
The design of models and tools to support Exploratory Search acquires more importance as the amount of information on the Web grows. The use of advanced search features is a viable approach for query exploration during Exploratory Search. However, the usage of advanced search features remains relatively low since Web search engines became popular, partially because of design decisions that ignore the complex and flexible nature of search activities. In this paper, we introduce Dico: a conceptual model for advanced search features for Exploratory Search, presenting and evaluating a set of guidelines created to support designers and evaluators to design better advanced search features, promoting its usage. Results from an evaluation activity with prospective designers indicated participants were able to make sense of Dico's guidelines, suggesting the guidelines as a promising artifact to support the evaluation of search engines.
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