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1. Semantic code clone detection for enterprise applications

   https://doi.org/10.1145/3341105.3374117  

   Svacina, Jan ; Simmons, Jonathan ; Cerny, Tomas ( March 2020 , Proceedings of the 35th Annual ACM Symposium on Applied Computing (SAC '20))

   null (Ed.)

   Enterprise systems are widely adopted across industries as methods of solving complex problems. As software complexity increases, the software's codebase becomes harder to manage and maintenance costs raise significantly. One such source of cost-raising complexity and code bloat is that of code clones. We proposed an approach to identify semantic code clones in enterprise frameworks by using control flow graphs (CFGs) and applying various proprietary similarity functions to compare enterprise targeted metadata for each pair of CFGs. This approach enables us to detect semantic code clones with high accuracy within a time complexity of O(n2) where n is equal to the number of CFGs composed in the enterprise application (usually around hundreds). We demonstrated our solution on a blind study utilizing a production enterprise application. 

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2. Intelligent token-based code clone detection system for large scale source code

   https://doi.org/10.1145/3338840.3355654  

   Elkhail, Abdulrahman Abu ; Svacina, Jan ; Cerny, Tomas ( January 2019 , Proceedings of the Conference on Research in Adaptive and Convergent Systems)

   A code clone refers to code fragments in the source code that are identical or similar to each other. Code clones lead difficulties in software maintenance, bug fixing, present poor design and increase the system size. Code clone detection techniques and tools have been proposed by many researchers, however, there is a lack of clone detection techniques especially for large scale repositories. In this paper, we present a token-based clone detector called Intelligent Clone Detection Tool (ICDT) that can detect both exact and near-miss clones from large repositories using a standard workstation environment. In order to evaluate the scalability and the efficiency of ICDT, we use the most recent benchmark which is a big benchmark of real clones, BigCloneBench. In addition, we compare ICDT to four publicly available and state-of-the-art tools. 

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3. TECCD: A Tree Embedding Approach for Code Clone Detection

   https://doi.org/10.1109/ICSME.2019.00025  

   Gao, Yi ; Wang, Zan ; Liu, Shuang ; Yang, Lin ; Sang, Wei ; Cai, Yuanfang ( September 2019 , IEEE International Conference on Software Maintenance and Evolution (ICSME))

   Clone detection techniques have been explored for decades. Recently, deep learning techniques has been adopted to improve the code representation capability, and improve the state-of-the-art in code clone detection. These approaches usually require a transformation from AST to binary tree to incorporate syntactical information, which introduces overheads. Moreover, these approaches conduct term-embedding, which requires large training datasets. In this paper, we introduce a tree embedding technique to conduct clone detection. Our approach first conducts tree embedding to obtain a node vector for each intermediate node in the AST, which captures the structure information of ASTs. Then we compose a tree vector from its involving node vectors using a lightweight method. Lastly Euclidean distances between tree vectors are measured to determine code clones. We implement our approach in a tool called TECCD and conduct an evaluation using the BigCloneBench (BCB) and 7 other large scale Java projects. The results show that our approach achieves good accuracy and recall and outperforms existing approaches. 

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4. Challenges in Behavioral Code Clone Detection

   https://doi.org/10.1109/SANER.2016.75  

   Su, Fang-Hsiang ; Bell, Jonathan ; Kaiser, Gail ( March 2016 , 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER))

   When software engineering researchers discuss "similar" code, we often mean code determined by static analysis to be textually, syntactically or structurally similar, known as code clones (looks alike). Ideally, we would like to also include code that is behaviorally or functionally similar, even if it looks completely different. The state of the art in detecting these behavioral clones focuses on checking the functional equivalence of the inputs and outputs of code fragments, regardless of its internal behavior (focusing only on input and output states). We argue that with an advance in dynamic code clone detection towards detecting behavioral clones (i.e., those with similar execution behavior), we can greatly increase the applications of behavioral clones as a whole for general program understanding tasks. 

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5. Challenges in Behavioral Code Clone Detection (Position Paper)

   https://doi.org/10.1109/SANER.2016.7  

   Fang-Hsiang Su, Jonathan Bell ( March 2016 , 10th International Workshop on Software Clones (IWSC), affiliated with IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER))

   When software engineering researchers discuss "similar" code, we often mean code determined by static analysis to be textually, syntactically or structurally similar, known as code clones (looks alike). Ideally, we would like to also include code that is behaviorally or functionally similar, even if it looks completely different. The state of the art in detecting these behavioral clones focuses on checking the functional equivalence of the inputs and outputs of code fragments, regardless of its internal behavior (focusing only on input and output states). We argue that with an advance in dynamic code clone detection towards detecting behavioral clones (i.e., those with similar execution behavior), we can greatly increase the applications of behavioral clones as a whole for general program understanding tasks. 

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