%0 Conference Paper %B Fourth International Workshop on Mining Software RepositoriesFourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007) %D 2007 %T Mining Software Repositories with iSPAROL and a Software Evolution Ontology %A Kiefer, Christoph %A Bernstein, Abraham %A Tappolet, Jonas %K database %K eclipse %K evoont %K java %K owl %K semantic %K sparql %X One of the most important decisions researchers face when analyzing the evolution of software systems is the choice of a proper data analysis/exchange format. Most existing formats have to be processed with special programs written specifically for that purpose and are not easily extendible. Most scientists, therefore, use their own database(s) requiring each of them to repeat the work of writing the import/export programs to their format. We present EvoOnt, a software repository data exchange format based on the Web Ontology Language (OWL). EvoOnt includes software, release, and bug-related information. Since OWL describes the semantics of the data, EvoOnt is (1) easily extendible, (2) comes with many existing tools, and (3) allows to derive assertions through its inherent Description Logic reasoning capabilities. The paper also shows iSPARQL -- our SPARQL-based Semantic Web query engine containing similarity joins. Together with EvoOnt, iSPARQL can accomplish a sizable number of tasks sought in software repository mining projects, such as an assessment of the amount of change between versions or the detection of bad code smells. To illustrate the usefulness of EvoOnt (and iSPARQL), we perform a series of experiments with a real-world Java project. These show that a number of software analyses can be reduced to simple iSPARQL queries on an EvoOnt dataset. %B Fourth International Workshop on Mining Software RepositoriesFourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007) %I IEEE %C Minneapolis, MN, USA %P 10 - 10 %@ 0-7695-2950-X %R 10.1109/MSR.2007.21 %> https://flosshub.org/sites/flosshub.org/files/28300010.pdf %0 Conference Paper %B Proceedings of the 2006 international workshop on Mining software repositories %D 2006 %T Detecting similar Java classes using tree algorithms %A Sager, Tobias %A Bernstein, Abraham %A Pinzger, Martin %A Kiefer, Christoph %K change analysis %K clones %K coogle %K eclipse %K famix %K java %K similarity %K software evolution %K software repositories %K source code %K tree similarity measures %X Similarity analysis of source code is helpful during development to provide, for instance, better support for code reuse. Consider a development environment that analyzes code while typing and that suggests similar code examples or existing implementations from a source code repository. Mining software repositories by means of similarity measures enables and enforces reusing existing code and reduces the developing effort needed by creating a shared knowledge base of code fragments. In information retrieval similarity measures are often used to find documents similar to a given query document. This paper extends this idea to source code repositories. It introduces our approach to detect similar Java classes in software projects using tree similarity algorithms. We show how our approach allows to find similar Java classes based on an evaluation of three tree-based similarity measures in the context of five user-defined test cases as well as a preliminary software evolution analysis of a medium-sized Java project. Initial results of our technique indicate that it (1) is indeed useful to identify similar Java classes, (2)successfully identifies the ex ante and ex post versions of refactored classes, and (3) provides some interesting insights into within-version and between-version dependencies of classes within a Java project. %B Proceedings of the 2006 international workshop on Mining software repositories %S MSR '06 %I ACM %C New York, NY, USA %P 65–71 %@ 1-59593-397-2 %U http://doi.acm.org/10.1145/1137983.1138000 %R http://doi.acm.org/10.1145/1137983.1138000 %> https://flosshub.org/sites/flosshub.org/files/65Detecting.pdf