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Detection of semantically similar code

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Abstract

The traditional similar code detection approaches are limited in detecting semantically similar codes, impeding their applications in practice. In this paper, we have improved the traditional metrics-based approach as well as the graph-based approach and presented a metrics-based and graph-based combined approach. First, source codes are represented as augmented system dependence graphs. Then, metrics-based candidate similar code extraction is performed to filter out most of the dissimilar code pairs so as to lower the computational complexity. After that, code normalization is performed on the candidate similar codes to remove code variations so as to detect similar code at the semantic level. Finally, program matching is performed on the normalized control dependence trees to output semantically similar codes. Experiment results show that our approach can detect similar codes with code variations, and it can be applied to large software.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Tiantian Wang, Kechao Wang, Xiaohong Su & Peijun Ma

  2. School of Software, Harbin University, Harbin, 150086, China

    Kechao Wang

Authors
  1. Tiantian Wang
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  2. Kechao Wang
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  3. Xiaohong Su
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  4. Peijun Ma
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Corresponding author

Correspondence to Tiantian Wang.

Additional information

Tiantian Wang is an associate professor at Harbin Institute of Technology, China. She received the PhD degree from Harbin Institute of Technology in 2009. Her current research interests are program analysis, automatic software debugging, and computer aided education.

Kechao Wang received the MS degree from Huazhong Univeristy of Science and Technology, China in 2006. Since 2012, he has been a PhD candidate in Computer Science Department of Harbin Institute of Technology. His current research interests are software fault localization and program analysis.

Xiaohong Su is a professor of Harbin Institute of Technology. She is a senior membership of China Computer Federation. Her main research interests are software bug detection, graphics and image processing, information fusion, and intelligent computation.

Peijun Ma is a professor of Harbin Institute of Technology. His main research interests are software engineering, information fusion, color matching, image processing, and intelligent control.

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Wang, T., Wang, K., Su, X. et al. Detection of semantically similar code. Front. Comput. Sci. 8, 996–1011 (2014). https://doi.org/10.1007/s11704-014-3430-1

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  • DOI: https://doi.org/10.1007/s11704-014-3430-1

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