2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) 2016
DOI: 10.1109/icmla.2016.0185
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Semantic Clone Detection Using Machine Learning

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Cited by 45 publications
(23 citation statements)
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“…It does not compute the complexity of the approach. 20 [28] It does not compute the complexity of the approach. This approach works only on java language.…”
Section: [2]mentioning
confidence: 99%
“…It does not compute the complexity of the approach. 20 [28] It does not compute the complexity of the approach. This approach works only on java language.…”
Section: [2]mentioning
confidence: 99%
“…Priyambadha and Rochimah provide a semantic clone detection technique based on program dependence graphs. Similarly, Sheneamer and Kalita consider abstract syntax trees and program dependency graphs. The innovative aspect of this approach is the representation of a pair of code fragments as a vector and the use of machine learning algorithms to detect clones.…”
Section: Related Workmentioning
confidence: 99%
“…One key factor that affects code quality is code cloning . Code clones are two fragments of source code that are syntactically or semantically identical to each other . According to Saini et al, it is possible to identify four different types of cloning concerning relations between two code fragments and based on the nature of the similarity in their text or meaning.…”
Section: Introductionmentioning
confidence: 99%
“…Replicating code sections and afterward reuse by sticking with or without minor changes or variations are commonplace activities in programming advancements. This kind of reuse approach of existing code is called code cloning and the stuck code fragment (with or without alterations) is referred to as the image of the unique [3], [4].…”
Section: Definition Of Code Clonementioning
confidence: 99%
“…Optimized Support Vector Machine is two special properties are that OSVM achieve: (i) High-generalization by maximizing the margin and (ii) Support an effective learning of non-linear methods by kernel trick. Optimized SVM helps to perform the mapping between the inputspace and feature-space to support non-linear classification issues [3], [18]. The kernel function is harmful for responsibility this by permitting the absence of the exact formulation of the method of mapping which can cause the issue of curse of dimensions.…”
Section: Proposed Approachmentioning
confidence: 99%