CVS release history data for detecting logical couplings

By Harald C. Gall, M. Jazayeri, J. Krajewski. Sixth International Workshop on Principles of Software Evolution, 2003. Proceedings., 2003

327 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/388180ea9c9bd617e5c82fa2003e250b3f62ccfa

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Citations in the corpus

A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction (2008, background): “, MAX/AVE_CHANGESET in Table 4, they are defined as follows: the change set of a file x is the number of files that have been committed together with file x (within a time frame of 2 minutes) to the repository (it is similar to the notion of co-changes [5]).”
Recovering traceability links in software artifact management systems using information retrieval methods (2007, methodology): “Approaches to recover traceability links between source code artifacts have also been proposed that use data mining techniques on software con.guration management repositories [Gall et al. 1998; 2003; Ying et al. 2004; Zimmermann et al. 2005].”
Mining version histories to guide software changes (2004, methodology): “The CVS history allows to detect more ﬁne-grained logical coupling between classes [10], ﬁles and functions [27].”
DynaMine: finding common error patterns by mining software revision histories (2005, background): “These items can be of any granularity; in the past co-change has been applied to changes in modules [17], files [5], classes [6, 18], and functions [38].”
Hipikat: a project memory for software development (2005, background): “While the information in software repositories can be mined to study the evolution of the software for the purpose of retrospective analysis (e.g., see [18]), it is also directly useful to a developer engaged in maintenance and evolution tasks.”
An empirical study on the evolution of design patterns (2007, methodology): “Historical co-change analysis has provided new opportunities for a number of issues: predict change propagation [24, 25], observe clone [12, 15] and crosscutting concern [5] evolution, identify crosscutting concerns [4, 7], detect of logical coupling between modules [10], find common error patterns ”
Mining version histories to guide software changes (2005, background): “In the past, researchers have leveraged evolutionary coupling to provide insights about a software system [12], [35].”