Clone Detection Using Abstract Syntax Suffix Trees

By Rainer Koschke, Raimar Falke, Pierre Frenzel. Working Conference on Reverse Engineering, 2006

403 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b4c2aff2716095696b266f6229a91baa70362a78

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Miltiadis Allamanis
2. Charles Sutton
3. Earl T. Barr
4. Premkumar T. Devanbu
5. Chanchal Kumar Roy
6. James R. Cordy
7. Ge Li
8. Michael Pradel

In Software

1. Charles Sutton
2. Miltiadis Allamanis
3. Earl T. Barr
4. Premkumar T. Devanbu
5. Chanchal Kumar Roy
6. Christian Bird
7. James R. Cordy
8. Ge Li

In Signal Processing

1. Chanchal Kumar Roy
2. James R. Cordy
3. Rainer Koschke
4. Miltiadis Allamanis
5. Charles Sutton
6. Earl T. Barr
7. Ge Li
8. Hongyu Zhang

Citations in the corpus

• Survey of Research on Software Clones (2006, neutral): “[49], the AST nodes are serialized in preorder traversal, a suffix tree is created for these serialized AST nodes, and the resulting maximally long AST node sequences are then cut according to their syntactic region so that only syntactically closed sequences remain.”
• Mining Semantic Loop Idioms from Big Code (2016, methodology): “Code clone detection using ASTs has also been studied extensively (Baxter et al. 1998; Jiang et al. 2007; Koschke et al. 2006).”
• Shaping program repair space with existing patches and similar code (2018, methodology): “Our approach depends on the search of similar code in a project, which is related to the work on code clone detection [23, 27, 31, 54].”
• Do code clones matter? (2009, result): “This is also noted for exact clones in [21] and is even more pronounced for inconsistent clones.”
• Comparison and evaluation of code clone detection techniques and tools: A qualitative approach (2009, methodology): “In the approach of Koschke et al. [72, 43], AST subtrees are serialized as AST node sequences for which a suffix tree is then constructed.”
• Mining idioms from source code (2014, methodology): “Code clone detection using ASTs has also been studied extensively [7, 13, 24, 30].”
• Comparison and Evaluation of Clone Detection Tools (2007, methodology): “The resulting AST node sequences are then cut into syntactic units based on the AST structure [40].”
• A Survey on Software Clone Detection Research (2007, background): “[153], the AST nodes are serialized in preorder traversal, a suffix tree is created for these serialized AST nodes, and the resulting maximally long AST node sequences are then cut according to their syntactic region so that only syntactically closed sequences remain.”
• Neural Detection of Semantic Code Clones Via Tree-Based Convolution (2019, background): “, developers are less reliant on the same piece of code), there is also common agreement that code clones should be detected and managed [1, 2].”
• NICAD: Accurate Detection of Near-Miss Intentional Clones Using Flexible Pretty-Printing and Code Normalization (2008, methodology): “Syntactical approaches [5, 18, 24, 31, 14] (also called tree-based approaches) are also related to ours in the sense that they are also parser-based and are aimed at syntactic clones.”