Code duplication is common in current programming-practice: programmers search for snippets of code, incorporate them into their projects and then modify them to their needs. In today's practice, no automated scheme is in place to inform both parties of any distant changes of the code. As code snippets continue to evolve both on the side of the user and on the side of the author, both may wish to benefit from remote bug fixes or refinements - authors may be interested in the actual usage of their code snippets, and researchers could gather information on clone usage. We propose to maintain a link between software clones across repositories and outline how the links can be created and maintained.

Detecting code duplication in large code bases, or even across project boundaries, is problematic due to the massive amount of data involved. Large-scale clone detection also opens new challenges beyond asking for the provenance of a single clone fragment, such as assessing the prevalence of code clones on the entire code base, and their evolution. We propose a set of lightweight techniques that may scale up to very large amounts of source code in the presence of multiple versions. The common idea behind these techniques is to use bad hashing to get a quick answer. We report on a case study, the Squeaksource ecosystem, which features thousands of software projects, with more than 40 million versions of methods, across more than seven years of evolution. We provide estimates for the prevalence of type-1, type-2, and type-3 clones in Squeaksource.