Abstract
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.