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  1. Proceedings of the 10th International Conference on Predictive Models in Software Engineering (PROMISE '14)
  2. Social metrics included in prediction models on software engineering: a mapping study
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A proposed method to evaluate and compare fault predictions across studies
Feature weighting techniques for CBR in software effort estimation studies: a review and empirical evaluation
Benchmarking effort estimation models using archetypal analysis
Defect prediction and software risk
On the influence of maintenance activity types on the issue resolution time
Exploiting prior-phase effort data to estimate the effort for the subsequent phases: a further assessment
Social metrics included in prediction models on software engineering: a mapping study
Analysing defect inflow distribution of automotive software projects
The potential benefit of relevance vector machine to software effort estimation
Effort estimation in agile software development: a systematic literature review

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

Content Provider ACM Digital Library
Author Côgo, Filipe Roseiro Gerosa, Marco Aurélio Steinmacher, Igor Ré, Reginaldo Wiese, Igor Scaliante
Abstract 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.
Starting Page 72
Ending Page 81
Page Count 10
File Format PDF
ISBN 9781450328982
DOI 10.1145/2639490.2639505
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-09-17
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Social network analysis Mapping study Prediction models Social metrics
Content Type Text
Resource Type Article
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