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The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. Objective: Examine the usage of data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Method: Data track papers were collected from the MSR data showcase track and through the manual inspection of older MSR proceedings. The use of data papers was established through manual citation searching followed by reading the citing studies and dividing them into strong and weak citations. Contrary to weak, strong citations truly use the data set of a data paper. Data papers were then manually clustered based on their content, whereas their strong citations were classified by hand according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. A survey study on 108 authors and users of data papers provided further insights regarding motivation and effort in data paper production, encouraging and discouraging factors in data set use, and future desired direction regarding data papers. Results: We found that 65% of the data papers have been used in other studies, with a longtail distribution in the number of strong citations. Weak citations to data papers usually refer to them as an example. MSR data papers are cited in total less than other MSR papers. A considerable number of the strong citations stem from the teams that authored the data papers. Publications providing Version Control System (VCS) primary and derived data are the most frequent data papers and the most often strongly cited ones. Enhanced developer data papers are the least common ones, and the second least frequently strongly cited. Data paper authors tend to gather data in the context of other research. Users of data sets appreciate high data quality and are discouraged by lack of replicability of data set construction. Data related to machine learning or derived from the manufacturing sector are two suggestions of the respondents for future data papers. Conclusions: Data papers have provided the foundation for a significant number of studies,

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Abstract-Cyberdiversity is a concept borrowed from biology and refers to the introduction of diversity into the different levels of a computer. This kind of diversity is used to avert attacks that can threat a large number of systems that share common characteristics and as a result common vulnerabilities. Currently, there are many methods that introduce cyberdiversity into systems but there is no attempt to measure the existing cyberdiversity. In this paper we introduce a novel approach that measures the existing diversity in software. To accomplish that, we specify three different metrics. The concept of our approach is to collect specific information and then process it in order to find distinct similarities or differences within software. To test our approach, we implemented a system, based on the client-server architecture. 1

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