JCoffee: Using Compiler Feedback to Make Partial Code Snippets Compilable

Gupta, Piyush; Mehrotra, Nikita; Purandare, Rahul

doi:10.1109/icsme46990.2020.00099

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…JCoffee is a Java code repair tool proposed by Piyush et al It tries to convert code fragments into compilable programs and works with any well-typed code fragments (class, function, or even an unenclosed group of statements) while making minimal changes to the input code fragment. JCoffee leverages compiler feedback to convert partial Java programs into their compilable counterparts by simulating the presence of missing surrounding code [32]. Figure 6 shows how using JCoffee fixes the code.…”

Section: Datasetsmentioning

confidence: 99%

“…, x FOR PEER REVIEW 11 of 17 (class, function, or even an unenclosed group of statements) while making minimal changes to the input code fragment. JCoffee leverages compiler feedback to convert partial Java programs into their compilable counterparts by simulating the presence of missing surrounding code [32]. Figure 6 shows how using JCoffee fixes the code.…”

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

SJBCD: A Java Code Clone Detection Method Based on Bytecode Using Siamese Neural Network

Wan,

Dong,

Zhou

et al. 2023

Applied Sciences

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Code clone detection is an important research topic in the field of software engineering. It is significant in developing software and solving software infringement disputes to discover code clone phenomenon effectively in and between software systems. In practical engineering applications, clone detection can usually only be performed on the compiled code due to the unavailability of the source code. Additionally, there is room for improvement in the detection effect of existing methods based on bytecode. Based on the above reasons, this paper proposes a novel code clone detection method for Java bytecode: SJBCD. SJBCD extracts opcode sequences from byte code files, use GloVe to vectorize opcodes, and builds a Siamese neural network based on GRU to perform supervised training. Then the trained network is used to detect code clones. In order to prove the effectiveness of SJBCD, this paper conducts validation experiments using the BigCloneBench dataset and provides a comparative analysis with four other methods. Experimental results show the effectiveness of the SJBCD method.

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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

SJBCD: A Java Code Clone Detection Method Based on Bytecode Using Siamese Neural Network

Wan,

Dong,

Zhou

et al. 2023

Applied Sciences

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“…Usually, Algorithm 1 Build Clone Classes From Clone Pairs x.parent ← x 3: end function x.parent ← FIND(x.parent) end if 20: end function the PDG is generated according to the information during compilation, while it will spend expensive time and space to compile all the projects in the code repository. We leverage a tool JCoffee [18] to complement each usage example code, and make it compilable. For the complemented usage example codes, we generate the PDGs by Soot [19].…”

Section: Program Slicingmentioning

confidence: 99%

Clone-based code method usage pattern mining

Xue,

Zhang,

2021

Preprint

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Programmers sometimes need to use some methods they are not familiar with like methods from other projects or written by others. However, it is hard to obtain usage information of a general method that is not a commonly used API or in the project with high impact, since this method may only have a brief comment and several usage examples. In this paper, we propose an approach to mine the usage information of a general method. Our approach collects the usage examples not only from the target method but also from its cloned methods. Based on the usage examples, we mine the usage of the target method by frequent subsequence mining technology after program slicing and code normalization. Our evaluation shows that our approach can mine 4 types of usage with an average precision of 0.65.

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“…Since our method needs the IR of the code snippet to build the semantic graph, our method requires that code snippets be able to be compiled. Therefore, we use tools such as JCoffe [11] to help them pass that obstacle.…”

Section: Semantic Feature Extractionmentioning

confidence: 99%

SEED: Semantic Graph based Deep detection for type-4 clone

Xue,

Liu,

Zeng

et al. 2021

Preprint

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Background: Type-4 clones refer to a pair of code snippets with similar functionality but written in different syntax, which challenges the existing code clone detection techniques. Previous studies, however, highly rely on syntactic structures and textual tokens, which cannot precisely represent the semantic information of code and might introduce nonnegligible noise into the detection models. Aims: To overcome these limitations, we explore an effective semantic-based solution for Type-4 clone detection. Additionally, we conducted an empirical study on the characteristics of Type-4 clone pairs. We found that NOT all tokens contain semantics that the Type-4 clone detection required. Operators and API calls emerge as distinct candidates for Type-4 code semantic representation. Method: To bridge this gap, we design a novel semantic graph based deep detection approach, called SEED. For a pair of code snippets, SEED constructs a semantic graph of each code snippet based on intermediate representation to represent the code functionality more precisely compared to the representations based on lexical and syntactic analysis. To accommodate the characteristics of Type-4 clones, a semantic graph is constructed focusing on the operators and API calls instead of all tokens. Then, SEED generates the feature vectors by using the graph deep neural network and performs code clone detection based on the similarity among the vectors. Results: Extensive experiments show that our approach significantly outperforms two baseline approaches over

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JCoffee: Using Compiler Feedback to Make Partial Code Snippets Compilable

Cited by 9 publications

References 9 publications

SJBCD: A Java Code Clone Detection Method Based on Bytecode Using Siamese Neural Network

SJBCD: A Java Code Clone Detection Method Based on Bytecode Using Siamese Neural Network

Clone-based code method usage pattern mining

SEED: Semantic Graph based Deep detection for type-4 clone

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