Towards a Big Data Curated Benchmark of Inter-project Code Clones

Abstract

Recently, new applications of code clone detection and search have emerged that rely upon clones detected across thousands of software systems. Big data clone detection and search algorithms have been proposed as an embedded part of these new applications. However, there exists no previous benchmark data for evaluating the recall and precision of these emerging techniques. In this paper, we present a Big Data clone detection benchmark that consists of known true and false positive clones in a Big Data inter-project Java repository. The benchmark was built by mining and then manually checking clones of ten common functionalities. The benchmark contains six million true positive clones of different clone types: Type-1, Type-2, Type-3 and Type-4, including various strengths of Type-3 similarity (strong, moderate, weak). These clones were found by three judges over 216 hours of manual validation efforts. We show how the benchmark can be used to measure the recall and precision of clone detection techniques.