On Using Network Science in Mining Developers Collaboration in Software Engineering: A Systematic Literature Review <s> Search libraries and In(Ex)clusion criteria <s> Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems. <s> BIB001 </s> On Using Network Science in Mining Developers Collaboration in Software Engineering: A Systematic Literature Review <s> Search libraries and In(Ex)clusion criteria <s> Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society. One crucial step when studying the structure and dynamics of networks is to identify communities: groups of related nodes that correspond to functional subunits such as protein complexes or social spheres. Communities in networks often overlap such that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure. However, the fact that many real networks have communities with pervasive overlap, where each and every node belongs to more than one group, has the consequence that a global hierarchy of nodes cannot capture the relationships between overlapping groups. Here we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks, including major biological networks such as protein-protein interaction and metabolic networks, and show that a large social network contains hierarchically organized community structures spanning inner-city to regional scales while maintaining pervasive overlap. Our results imply that link communities are fundamental building blocks that reveal overlap and hierarchical organization in networks to be two aspects of the same phenomenon. <s> BIB002 </s> On Using Network Science in Mining Developers Collaboration in Software Engineering: A Systematic Literature Review <s> Search libraries and In(Ex)clusion criteria <s> Correcting software defects accounts for a significant amount of resources such as time, money and personnel. To be able to focus testing efforts where needed the most, researchers have studied statistical models to predict in which parts of a software future defects are likely to occur. By studying the mathematical relations between predictor variables used in these models, researchers can form an increased understanding of the important connections between development activities and software quality. Predictor variables used in past top-performing models are largely based on file-oriented measures, such as source code and churn metrics. However, source code is the end product of numerous interlaced and collaborative activities carried out by developers. Traces of such activities can be found in the repositories used to manage development efforts. In this paper, we investigate statistical models, to study the impact of social structures between developers and end-users on software quality. These models use predictor variables based on social information mined from the issue tracking and version control repositories of a large open-source software project. The results of our case study are promising and indicate that statistical models based on social information have a similar degree of explanatory power as traditional models. Furthermore, our findings suggest that social information does not substitute, but rather augments traditional product and process-based metrics used in defect prediction models. <s> BIB003 </s> On Using Network Science in Mining Developers Collaboration in Software Engineering: A Systematic Literature Review <s> Search libraries and In(Ex)clusion criteria <s> We conduct a statistical analysis on the social networks of contributors in Open Source Software (OSS) communities using datasets collected from two most fast-growing OSS social interaction sites, Github.com and Ohloh.net. Our goal is to analyze the connectivity structure of the social networks of contributors and to investigate the effect of the different social tie structures on developers' overall productivity to OSS projects. We first analyze the general structure of the social networks, e.g., graph distances and the degree distribution of the social networks. Our analysis confirms that the social networks of OSS communities follow power-law degree distributions and exhibit small-world characteristics. However, the degree mixing pattern shows that high degree nodes tend to connect more with low degree nodes, suggesting collaborations between experts and newbie developers. Second, we study the correlation between graph degrees and the productivity of the contributors in terms of the amount of contribution and commitment to OSS projects. The analysis demonstrates evident influence of the social ties on the developers' overall productivity. <s> BIB004
We identified the following search libraries as a basis for the search process. The libraries are: (1) The ACM digital library, (2) The IEEExplore, (3) The Springer, and (4) The ScienceDirect. The initial studies resulted from the search process was 6080 that we sifted based on the title and the abstract content. The result was 199 related initial primary studies that we read in order to apply the inclusion and exclusion criteria. Table 1 and Figure 3 show more details about the number of selected studies in each step across the search libraries. Table 2 shows the inclusion criteria. Any included study should satisfy all of the inclusion criteria in the table. Table 3 shows the exclusion criteria along with the number of excluded studies due to each exclusion criterion. Research that uses network analysis in studying software engineering practices or artifacts or processes. Empirical study that provides quantitative results. It covers minable human aspects and/or minable software artifacts I BIB002 It is a peer reviewed publications. Table 2 : The inclusion criteria applied in this SLR. earlier primary study was published in 2005, a few years after the emergence of network science papers. Any study that is not in English. Replication studies, invited papers, lessons learned, technical papers, and position papers. Visualization tools, process engineering, and controlled experiment papers. (14) E BIB002 Theoretical models that have not been validated empirically with real systems. (10) E BIB004 Papers that do not cover the minable human aspects in software engineering. (41) E BIB001 Duplicated papers or initial work that has been extended later. (4) E BIB003 Papers that only construct networks from data without analysis. (8) Table 3 : The exclusion criteria along with the number of excluded studies due to each exclusion criterion. After applying the inclusion and exclusion criteria, we got the following primary studies. For the ACM digital library and the IEEE, we had 15 studies selected from each. For the SciencDirect and the Springer, we had 3 and 2 primary studies, respectively. The included primary studies in this SLR are shown in Table 4 along with their venues. Figure 5 shows the number of primary studies per venue. The Others represents all venues with only one primary study. From Figure 5 and from Table 4 , we noticed that major software engineering conferences like ICSE and FSE have the highest number of primary studies. Also, from the information in the table and the figure, we see that there is 31 primary studies published in conference proceedings and the rest, 4 studies, are published in journals.