Abstract
In this paper we apply the social network concept of core-periphery structure to the socio-technical structure of a software development team. We propose a socio-technical pattern that can be used to locate emerging coordination problems in Open Source projects. With the help of our tool and method called TESNA, we demonstrate a method to monitor the socio-technical core-periphery movement in Open Source projects. We then study the impact of different core-periphery movements on Open Source projects. We conclude that a steady core-periphery shift towards the core is beneficial to the project, whereas shifts away from the core are clearly not good. Furthermore, oscillatory shifts towards and away from the core can be considered as an indication of the instability of the project. Such an analysis can provide developers with a good insight into the health of an Open Source project. Researchers can gain from the pattern theory, and from the method we use to study the core-periphery movements.
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Acknowledgements
The authors thank Jeff Hicks and the anonymous reviewers for their extensive feedback that was really helpful in structuring and improving this paper.
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Appendices
Appendix A
To represent the people and the software in an understandable way, we cluster the software into clusters according to the class level dependencies (Fernandez, 1998) and display who is working at which cluster for the particular time period of the data.
The algorithm we use is shown in Table A1.
In the above algorithm the vertical buses are those elements in the SM whose ‘vertical dependencies’ (ones in the vertical columns of the SM matrix) to other elements is more than a specific threshold (MacCormack et al., 2006). These elements are important, as they are common functions called by other modules (MacCormack et al., 2006). Once these vertical buses are identified a DependencyCost is assigned to each module, element of SM. This DependencyCost is assigned as follows:
Equation 1: Calculation of the Dependency Cost (taken from (MacCormack et al., 2006))
where d ij is a binary variable indicating dependency between i and j (so in our case it is SM(i, j)+SM(j, i)), n is the size of the cluster when i and j located within the cluster and N is the size of the SM matrix (when i and j are not located in the same cluster). λ is a user-defined parameter and is found by trial and error (depending on the variation of the results) to be optimum at 2. Adding an element to a cluster increases the cost of other dependencies in the cluster (as the size of the cluster increases); hence an element is only added to a cluster when the reduction in the sum of DependencyCosts with the element exceeds the added costs borne by other dependencies (MacCormack et al., 2006).
Now the summation of the DependencyCosts of all the elements of SM gives us the ClusteredCost of the matrix for the particular iteration. Hence the ClusteredCost can be expressed as:
Equation 2: Calculation of Clustered Cost (adapted from ( Fernandez, 1998 ) and ( MacCormack et al., 2006 ))
In equation 2 CC(i) represents the Clustered Cost for the element SM(i, j).
Appendix B
In order to gauge the success of the Open Source projects we studied in this paper, we looked into literature on measuring Open Source success. We came up with two papers, the often cited Crowston et al. (2006a) and the latest and most comprehensive work on the subject, namely Subramaniam et al. (2009). The data collection model used by Crowston et al. (2006a) involves studying the bug tracker and the mailing list of the projects. Since some of the projects (JAIM, Ivy-ssh) do not have either, we decided to use the data collection model of Subramaniam et al. (2009). Subramaniam et al. (2009) measure an Open Source project's success by measuring user interest, project interest and developer interest. They measure user interest by calculating the number of project downloads. We use project downloads and page views (as done by Crowston et al. (2006a)) to measure user interest. We also add the download and page view trend in order to get a more time-variant perspective of user interest. In order to measure the developer interest in the project, Subramaniam et al. (2009) count the number of active developers in the project. We do something similar, and calculate the average number of active developers (per year) contributing to the project. We gather these data from the project's software repository. In order to measure project activity, Subramaniam et al. (2009) calculate the number of files released in the project. We do the same and also augment these data with the project status data taken from Sourceforge (Sourceforge, Retrieved 1st March 2009). The results are shown in Table B1.
When studying user interest in Table B1, one has to keep in mind the findings of Crowston et al. (2006a) (Table 7, p. 142) shown briefly in Table B2.
When one compares the values of user interest with those in Table B2, it becomes clear that JAIM and Ivy-ssh have not generated much user interest. On the other hand, if one just observes the download and page view trends, then we observe that the projects EIRC, JAIM, Ivy-ssh, Eclipse plugin profiler and Megameknet have downward (D) trends, indicating waning user interest in the projects. On studying the project activity in Table B1, we observe that JAIM, Ivy-ssh and Megameknet have less than three version releases. While observing the development status, one sees that Ivy-ssh, Eclipse plugin profiler and Megameknet are Inactive (7), while JAIM has the development status of beta (4). Finally, looking at the average number of developers in a year working on the project, we observe that EIRC, JAIM and Ivy-ssh have less than one developer on an average working on the project, whereas Eclipse plugin profiler has less than two developers working on the project per year.
Aggregating the three measures of success as described by Subramaniam et al. (2009), we notice that JAIM (though still in beta) and Ivy-ssh are clearly not healthy, whereas EIRC, Eclipse plugin profiler and Megameknet have poor health and are inactive. On the other hand JBoss, jEdit and Jython are clearly healthy and doing well. Here, we must mention that the downturn in the page views for JBoss could be because the project has shifted to another location.
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Amrit, C., van Hillegersberg, J. Exploring the impact of socio-technical core-periphery structures in open source software development. J Inf Technol 25, 216–229 (2010). https://doi.org/10.1057/jit.2010.7
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DOI: https://doi.org/10.1057/jit.2010.7