Earticle

Most traditional code clone detections have less accurate results because they ignore the structure of the program itself, and some of them really think about it by creating a complex syntax tree but leading to a high time complexity. Confronting such situation, this paper proposes an efficient metric vector-based code clone detection method using function-calling tree. Considering the two program code to be detected, feature vectors in all defined functions of the two different code are extracted first. Then, two function-calling trees are created according to the function-calling process and node matches each other between two trees, at the same time, the matching similarities are calculated. Finally, by using the bottom-up approach and combining similarity values of all child nodes, the detection can get the similarity of the two program code to be detected. Our experiment selects a set of typical code sample to measure and the results demonstrate that, compared the famous JPlag system, it shows better detection effect.

Software systems often contain plenty of code clones, which bring significant impact on software development and maintenance. Tracking clones in the evolution process is essential to analyzing clones, since we cannot understand clone phenomenon well just rely on the clone detection results of single version. We developed a function clone genealogy extractor, cGen, which can track clones across multiple versions to extract type-1 and type-2 function clone genealogies. By using cGen, we examine nine open source C projects and analyze their evolution. Our study shows that cGen can efficiently extract clone genealogies from multiple versions of a project, and provide support for clone evolution analysis.

Software system’s clones are usually two aspects influence on software maintenance and management. One is some clones are effective and can reuse. The other is some clones are unsafe and need revise or reconfiguration. The reason is that the changes of code clones are different. How to determine the clones' attribute of effective or unsafe, it need to track clone changes in the evolution versions of a software system. We firstly find the clones and clone groups in multiple versions of a software system using a clone detector FCD, and construct the mapping of every adjacent version basing on the similarity of code clones, then extract clone genealogies in the software system. The clone genealogies’ results are efficient and can help us analysis the code clone changes and get the attribute about effective and unsafe.

This paper researched on the quality of code clone in the software, evaluated the code clone quality of the current versions. Then using Bayesian network to train the existing sample data to get the prediction model of code clone that is able to predict the quality. The prediction results are able to help developers decide which code clone should be reconstructed or efficiently reused. The experiment shows that the method can be used to predict the quality of code clone in software more accurately.

Domain analysis is the process of analyzing related software systems in a domain to find their common and variable parts. In the case of device drivers, they are highly suitable for domain analysis because device drivers of the same domain are implemented similarly for each device and each system that they support. Considering this characteristic, this paper introduces a new approach to the domain analysis of device drivers. Our method uses a code clone detection technique to extract similarity among device drivers of the same domain. To examine the applicability of our method, we investigated whole device drivers of a Linux source. Results showed that many reusable similar codes can be discerned by the code clone detection method. We also investigated if our method is applicable to other kernel sources. However, the results show that the code clone detection method is not useful for the domain analysis of all kernel sources. That is, the applicability of the code clone detection method to domain analysis is a peculiar feature of device drivers.