Archeology of code duplication: recovering duplication chains from small duplication fragments

Wettel, Richard; Marinescu, Radu

doi:10.1109/synasc.2005.20

Cited by 47 publications

(33 citation statements)

References 12 publications

(19 reference statements)

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“…Our matching algorithm is similar to that of DuDe [13] with two differences. First, our technique is language dependent due to the usage of an abstract syntax tree (AST) parser for accurate tokenization of the code elements.…”

Section: ) Algorithmmentioning

confidence: 99%

“…First, our technique is language dependent due to the usage of an abstract syntax tree (AST) parser for accurate tokenization of the code elements. The parser is different from Dude [13], which detects code elements using regular expressions. Second, we only support Type-1 and Type-2 clones whereas DuDe supports Type-1, Type-2, and some Type-3 clones.…”

Section: ) Algorithmmentioning

confidence: 99%

“…Our code clone detection implementation is similar to DuDe [13], which is a text-based tool. DuDe relies on combining non-gapped clones into a single large clone to support Type-3 clones.…”

Section: B Code Clone Detectionmentioning

confidence: 99%

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CloCom: Mining existing source code for automatic comment generation

Wong

Liu

Tan

2015

2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

131

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Abstract-Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences.In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.

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Section: ) Algorithmmentioning

confidence: 99%

Section: ) Algorithmmentioning

confidence: 99%

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CloCom: Mining existing source code for automatic comment generation

Wong

Liu

Tan

2015

2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

131

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show abstract

“…There are many methods of code similarity detection known in the literature, such as string-based [6,7], token-based [2,3,8,9], or Program Dependence Graph-based [1,13,14]. Every method focuses on different features of code.…”

Section: Hardmentioning

confidence: 99%

Detecting similarity of R functions via a fusion of multiple heuristic methods

Bartoszuk¹,

Gągolewski²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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In this paper we describe recent advances in our R code similarity detection algorithm. We propose a modification of the Program Dependence Graph (PDG) procedure used in the GPLAG system that better fits the nature of functional programming languages like R. The major strength of our approach lies in a proper aggregation of outputs of multiple plagiarism detection methods, as it is well known that no single technique gives perfect results. It turns out that the incorporation of the PDG algorithm significantly improves the recall ratio, i.e. it is better in indicating true positive cases of plagiarism or code cloning patterns. The implemented system is available as web application at http://SimilaR.Rexamine.com/.

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“…To answer the question whether code duplication may create problems during refactoring, we computed both internal and external code duplication using the clone detector from [Wettel and Marinescu 2005]. This clone detector was preferred as it is very easy to configure, works at a lexical-syntax level (as opposed to more complex clone detectors that need semantic analysis), is robust, and freely available.…”

Section: Code Duplication Analysismentioning

confidence: 99%