A Framework for Studying Clones In Large Software Systems

Abstract

Clones are code segments that have been created by copying-and-pasting from other code segments. Clones occur often in large software systems. It is reported that 5 to 50% of the source code of a large software system is cloned. A major challenge when studying code cloning in large software systems is handling the large amount of clone candidates produced by leading edge clone detection tools. For example, the CCFinder, clone detection tool, produces over 7 million pairs of clone candidates for the Linux kernel (which consists of over 4 MLOC). Moreover, the output of clone detection tools grows rapidly as a software system evolves. Researchers and developers need tools to help them study the large amount of clone data in order to better understand the clone phenomena in large systems. In this paper, we propose a data mining framework to help researchers cope with the large amount of data produced by clone detection tools. We propose techniques to reduce, abstract and highlight the most interesting data produced by clone detection tools. Our framework also introduces a visualization tool which allows users to query and explore clone data at various abstraction levels. We demonstrate our framework on a case study of the clone phenomena in the Linux kernel.

The vision of software clone management: Past, present, and future (Keynote paper)

“…Jiang et al [57] extended the idea of cohesion and coupling to code clones and proposed a visualization that uses shape and color to encode the metric values. They also developed a framework [57] for large scale clone analysis and proposed another visualization, called a clone system hierarchical graph that shows the distribution of clones in different parts (with respect to the file-system hierarchy) of a system. Fukushima et al [36] developed another visualization using graph drawing to identify diffused (scattered) clones.…”

Analysis and visualization for clone refactoring

“…Clone Clone Granularity Relation Tree Map [1], [14] F, S G Scatter Plot [1], [14], [17] F, S, C P System Model View [14] F, S P Clone System Hierarchical Tree [7] F, S P, G Hasse Diagram [9] F G Clone Group Family Enumeration [14] F G Duplication Web [14] F P Dependency Graph [11] S P Hierarchical Dependency Graph [1] S P Clone Coupling and Cohesion [8] S Sc Metric Graph [17] C G Clone Cluster View [4] C G Hyper-Linked Web Page [2], [10] C G Clone Visualizer View [16] F, C G Stacked Bar Chart [19] F, C G Line Chart [19] F, C G Edge Bundle View [5] F, S P Here, F = file, S = sub-system or sub-directory, C = code segment P = clone-pair, G = clone-group, Sc = super-clone…”

A Topic Modeling Approach for Code Clone Detection

“…This requires developers working with such tools to manually classify and verify the detected candidate clones [8,30], a process that is often described as time-consuming and error-prone [25]. In addition, there is still a lack of adequate support for large-scale systems, where clones are likely to spread over several code modules [13,21].…”