Cross-Language Source Code Plagiarism Detection using Explicit Semantic Analysis and Scored Greedy String Tilling

Foltýnek, Tomáš; Všianský, Richard; Meuschke, Norman; Dlabolová, Dita; Gipp, Béla

doi:10.1145/3383583.3398594

Cited by 9 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To extract the semantic similarity between the codes and mutants, the proposed technique uses the well‐known greedy string tiling (GST) 27 approach that traverses the string pairs to identify the semantic relationship between them. Hence, to regulate the process, the strings in the program code are generated into a pattern, and then the pattern is traversed element‐by‐element to mark the matching strings.…”

Section: Proposed Methodologymentioning

confidence: 99%

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Jammalamadaka

Parveen

2021

Softw Pract Exp

View full text Add to dashboard Cite

Mutation testing is a significant software testing approach that identifies the faults present in the source codes and the potentiality of the test cases in detecting the mutated codes. In this testing approach, equivalent mutant detection is complicated as the test cases will not identify the mutated codes from the source program. To overcome this problem, the article proposes a novel hybrid strategy known as the hybrid wavelet convolutional rain optimization (HWCRO) to classify the equivalent mutants present in the source codes accurately. The proposed technique considers three different classes of equivalent mutants based on the RIPR model and exactly identifies the mutated code. Initially, the features such as the semantic similarity and the information entropy are extracted, and these features are given as the input to the wavelet convolutional neural network (wCNN) classifier. The dimensions of the features are reduced in the convolutional layers using the wavelet function, which enhances the classifier's performance. To improve the classification accuracy, the loss function is minimized with an adaptive rain optimization algorithm (ROA) that iteratively tunes the parameter of wCNN. The proposed approach is compared with the existing classification techniques based on the parameters such as precision, recall, f1-score, and accuracy, and the simulation results yielded 85.17% accuracy value for the proposed approach.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Jammalamadaka

Parveen

2021

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…In such a manner, program similarity can be calculated in linear time. Cosine correlation is employed by Flores et al [38] and Foltynek et al [39]. Latent semantic analysis is employed by Ullah et al [40] and Cosma and Joy et al [41].…”

Section: Literature Reviewmentioning

confidence: 99%

Maintaining Academic Integrity in Programming: Locality-Sensitive Hashing and Recommendations

Karnalim

2023

Education Sciences

View full text Add to dashboard Cite

Not many efficient similarity detectors are employed in practice to maintain academic integrity. Perhaps it is because they lack intuitive reports for investigation, they only have a command line interface, and/or they are not publicly accessible. This paper presents SSTRANGE, an efficient similarity detector with locality-sensitive hashing (MinHash and Super-Bit). The tool features intuitive reports for investigation and a graphical user interface. Further, it is accessible on GitHub. SSTRANGE was evaluated on the SOCO dataset under two performance metrics: f-score and processing time. The evaluation shows that both MinHash and Super-Bit are more efficient than their predecessors (Cosine and Jaccard with 60% less processing time) and a common similarity measurement (running Karp-Rabin greedy string tiling with 99% less processing time). Further, the effectiveness trade-off is still reasonable (no more than 24%). Higher effectiveness can be obtained by tuning the number of clusters and stages. To encourage the use of automated similarity detectors, we provide ten recommendations for instructors interested in employing such detectors for the first time. These include consideration of assessment design, irregular patterns of similarity, multiple similarity measurements, and effectiveness–efficiency trade-off. The recommendations are based on our 2.5-year experience employing similarity detectors (SSTRANGE’s predecessors) in 13 course offerings with various assessment designs.

show abstract

“…On the other hand, the algorithm is known to be robust and precise 9 and still attracts attention within the research community. For example, a modified variant, the scored GST, is used in the cross‐lingual source code plagiarism detection system EsaGST, 20 based on explicit semantic analysis.…”

Section: Greedy String Tiling and Karp–rabin Algorithmsmentioning

confidence: 99%

Comparison of parallel central processing unit‐ and graphics processing unit‐based implementations of greedy string tiling algorithm for source code plagiarism detection

Misic

Tomašević

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for plagiarism. Source code similarity detection tools are used to prevent such misbehavior. Parallel processing has recently become a viable technique for speeding up the processing of large workloads. This article examines the parallelization of a source code similarity detection method based on the greedy string tiling and Karp–Rabin algorithms. Both CPU and GPU parallelization approaches are discussed. The CPU implementation uses Pthreads, whereas the GPU implementation employs CUDA. Depending on the evaluated dataset which consists of real student assignment codes, speedups of up to seven times over the sequential version of the code are achieved. Evaluation results on both platforms are compared and discussed in detail.

show abstract

Cross-Language Source Code Plagiarism Detection using Explicit Semantic Analysis and Scored Greedy String Tilling

Cited by 9 publications

References 3 publications

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Maintaining Academic Integrity in Programming: Locality-Sensitive Hashing and Recommendations

Comparison of parallel central processing unit‐ and graphics processing unit‐based implementations of greedy string tiling algorithm for source code plagiarism detection

Contact Info

Product

Resources

About