&lt;i&gt;CLCMiner&lt;/i&gt;: Detecting Cross-Language Clones without Intermediates

Cheng, Xuefei; Peng, Zhiming; Jiang, Lingxiao; Zhong, Hao; Yu, Haibo; Zhao, Jianjun

doi:10.1587/transinf.2016edp7334

Cited by 23 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The state-of-the-art semantic clone detection techniques [95], [101] rely on programs that yield the same outputs using dynamic code similarity detection [29], [51], [82], and identify similar behaviors of different programs by comparing instruction-level execution [21]. These approaches are precise, but not scalable, and have limitations for practical usage.…”

Section: Resultsmentioning

confidence: 99%

“…The approach is applicable to any languages that can be compiled to LLVM bitcode. Cheng et al [101] conceptualized the notion of detecting similarities in sets of components written in different languages, using Natural Language Processing techniques to mine projects' revision histories. Vislavski et al [95] designed a tool, LICCA, for cross-language clone detection that is based on intermediate program representation to unify semantically similar code fragments.…”

Section: B Clone Detection Across Languagesmentioning

confidence: 99%

See 1 more Smart Citation

Source Code Comments: Overlooked in the Realm of Code Clone Detection

Kuttal,

Ghosh

2020

Preprint

View full text Add to dashboard Cite

Reusing code can produce duplicate or nearduplicate code clones in code repositories. Current code clone detection techniques, like Program Dependence Graphs, rely on code structure and their dependencies to detect clones. These techniques are expensive, using large amounts of processing power, time, and memory. In practice, programmers often utilize code comments to comprehend and reuse code, as comments carry important domain knowledge. But current code detection techniques ignore code comments, mainly due to the ambiguity of the English language. Recent advances in information retrieval techniques may have the potential to utilize code comments for clone detection. We investigated this by empirically comparing the accuracy of detecting clones with solely comments versus solely source code (without comments) on the JHotDraw package, which contains 315 classes and 27K lines of code. To detect clones at the file level, we used a topic modeling technique, Latent Dirichlet Allocation, to analyze code comments and GRAPLE -utilizing Program Dependency Graph -to analyze code. Our results show 94.86% recall and 84.21% precision with Latent Dirichlet Allocation and 28.7% recall and 55.39% precision using GRAPLE. We found Latent Dirichlet Allocation generated false positives in cases where programs lacked quality comments. But this limitation can be addressed by using a hybrid approach: utilizing code comments at the file level to reduce the clone set and then using Program Dependency Graph-based techniques at the method level to detect precise clones. Our further analysis across Java and Python packages, Java Swing and PyGUI, found a recall of 74.86% and a precision of 84.21%. Our findings call for reexamining the assumptions regarding the use of code comments in current clone detection techniques.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: B Clone Detection Across Languagesmentioning

confidence: 99%

Source Code Comments: Overlooked in the Realm of Code Clone Detection

Kuttal,

Ghosh

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…9. Cheng (2017) [29] proposed the first-ever technique to process cross-language clone detection for Java and C# languages without using intermediate code that does not share common libraries. The method has following issues…”

Section: A Tool Called Crolsimmentioning

confidence: 99%

Detection and Classification of Cross-language Code Clone Types by Filtering the Nodes of ANTLR-generated Parse Tree

Ankali¹,

Parthiban²

2021

IJISA

View full text Add to dashboard Cite

A complete and accurate cross-language clone detection tool can support software forking process that reuses the more reliable algorithms of legacy systems from one language code base to other. Cross-language clone detection also helps in building code recommendation system. This paper proposes a new technique to detect and classify cross-language clones of C and C++ programs by filtering the nodes of ANTLR-generated parse tree using a common grammar file, CPP14.g4. Parsing the input files using CPP14.g4 provides all the lexical and semantic information of input source code. Selective filtering of nodes performs serialization of two parse trees. Vector representation using term frequency inverse document frequency (TF-IDF) of the resultant tree is given as an input to cosine similarity to classify the clone types. Filtered parse tree of C and C++ increases the precision from 51% to 61%, and matching based on renaming the input/output expressions provides average precision of 91.97% and 95.37% for small scale and large scale repositories respectively. The proposed cross-language clone detection exhibits the highest precision of 95.37% in finding all types of clones (1, 2, 3 and 4) for 16,032 semantically similar clone pairs of C and CPP codes.

show abstract

“…Some approaches utilize NLP to detect the similarity of cross-language source code. ese techniques mainly include n-gram model [31], LSA (Latent Semantic Analysis) [32], BOW (Bag of Words) [33], component analysis, and multiple logistic regression models [34].…”

Section: Cross-language Source Code Similarity Detectionmentioning

confidence: 99%

Flowchart-Based Cross-Language Source Code Similarity Detection

Feng

Liu

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

Source code similarity detection has various applications in code plagiarism detection and software intellectual property protection. In computer programming teaching, students may convert the source code written in one programming language into another language for their code assignment submission. Existing similarity measures of source code written in the same language are not applicable for the cross-language code similarity detection because of syntactic differences among different programming languages. Meanwhile, existing cross-language source similarity detection approaches are susceptible to complex code obfuscation techniques, such as replacing equivalent control structure and adding redundant statements. To solve this problem, we propose a cross-language code similarity detection (CLCSD) approach based on code flowcharts. In general, two source code fragments written in different programming languages are transformed into standardized code flowcharts (SCFC), and their similarity is obtained by measuring their corresponding SCFC. More specifically, we first introduce the standardized code flowchart (SCFC) model to be the uniform flowcharts representation of source code written in different languages. SCFC is language-independent, and therefore, it can be used as the intermediate structure for source code similarity detection. Meanwhile, transformation techniques are given to transform source code written in a specific programming language into an SCFC. Second, we propose the SCFC-SPGK algorithm based on the shortest path graph kernel to measure the similarity between two SCFCs. Thus, the similarity between two pieces of source code in different programming languages is given by the similarity between SCFCs. Experimental results show that compared with existing approaches, CLCSD has higher accuracy in cross-language source code similarity detection. Furthermore, CLCSD cannot only handle common source code obfuscation techniques used by students in computer programming teaching but also obtain nearly 90% accuracy in dealing with some complex obfuscation techniques.

show abstract

<i>CLCMiner</i>: Detecting Cross-Language Clones without Intermediates

Cited by 23 publications

References 25 publications

Source Code Comments: Overlooked in the Realm of Code Clone Detection

Source Code Comments: Overlooked in the Realm of Code Clone Detection

Detection and Classification of Cross-language Code Clone Types by Filtering the Nodes of ANTLR-generated Parse Tree

Flowchart-Based Cross-Language Source Code Similarity Detection

Contact Info

Product

Resources

About