Source Code Similarity Research Articles

Automated Hints Generation for Investigating Source Code Plagiarism and Identifying The Culprits on In-Class Individual Programming Assessment

Most source code plagiarism detection tools only rely on source code similarity to indicate plagiarism. This can be an issue since not all source code pairs with high similarity are plagiarism. Moreover, the culprits (i.e., the ones who plagiarise) cannot be differentiated from the victims even though they need to be educated further on different ways. This paper proposes a mechanism to generate hints for investigating source code plagiarism and identifying the culprits on in-class individual programming assessment. The hints are collected from the culprits’ copying behaviour during the assessment. According to our evaluation, the hints from source code creation process and seating position are 76.88% and at least 80.87% accurate for indicating plagiarism. Further, the hints from source code creation process can be helpful for indicating the culprits as the culprits’ codes have at least one of our predefined conditions for the copying behaviour.

Similarity Code File Detection Model Based on Frequent Itemsets

In order to improve the efficiency and accuracy of program source code similarity detection, an improvement on the method of code detection is made according to some deficiencies of the current research. A similar code detection model based on frequent item sets is proposed. The model constructs frequent items set data to discover repetitive code collections and automatically divide file similarity attribution. The algorithm model does not need to consider the type of the code in the detection process, and has wide applicability, not only can detect the code files of different programming languages and grammars, but also can mark out similar codes and statistic the results. Simultaneously, through experimental comparison, it is proved that the model has high accuracy and processing efficiency.

An Approach to Source Code Plagiarism Detection Based on Abstract Implementation Structure Diagram

Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Based on the analysis of the characteristics and defects of the existing program code similarity detection system, a method of source code similarity detection based on Abstract Implementation Structure Diagram (AISD) is proposed. The source code modelling and format into an abstract implementation structure diagram, and forming structural feature strings and variable reference relationship sequences by extracting structural features and variable position features. We calculate the overall similarity by calculating structural similarity and variable similarity. The results demonstrate that the performance of the proposed AISD-based approach overcomes other approaches on the same source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.

A comparison of code similarity analysers

Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.

A novel kernel to predict software defectiveness

Although the software defect prediction problem has been researched for a long time, the results achieved are not so bright. In this paper, we propose to use novel kernels for defect prediction that are based on the plagiarized source code, software clones and textual similarity. We generate precomputed kernel matrices and compare their performance on different data sets to model the relationship between source code similarity and defectiveness. Each value in a kernel matrix shows how much parallelism exists between the corresponding files of a software system chosen. Our experiments on 10 real world datasets indicate that support vector machines (SVM) with a precomputed kernel matrix performs better than the SVM with the usual linear kernel in terms of F-measure. Similarly, when used with a precomputed kernel, the k-nearest neighbor classifier (KNN) achieves comparable performance with respect to KNN classifier. The results from this preliminary study indicate that source code similarity can be used to predict defect proneness.

Computation of Program Source Code Similarity by Composition of Parse Tree and Call Graph

This paper proposes a novel method to compute how similar two program source codes are. Since a program source code is represented as a structural form, the proposed method adopts convolution kernel functions as a similarity measure. Actually, a program source code has two kinds of structural information. One is syntactic information and the other is the dependencies of function calls lying on the program. Since the syntactic information of a program is expressed as its parse tree, the syntactic similarity between two programs is computed by a parse tree kernel. The function calls within a program provide a global structure of a program and can be represented as a graph. Therefore, the similarity of function calls is computed with a graph kernel. Then, both structural similarities are reflected simultaneously into comparing program source codes by composing the parse tree and the graph kernels based on a cyclomatic complexity. According to the experimental results on a real data set for program plagiarism detection, the proposed method is proved to be effective in capturing the similarity between programs. The experiments show that the plagiarized pairs of programs are found correctly and thoroughly by the proposed method.

A Token Oriented Measurement Method of Source Code Similarity

In order to help teachers to identify plagiarism in student assignment submissions among students’ Source code quickly and accurately, this paper discusses a measurement method of Source code similarity. In the proposed algorithm, firstly, both of token oriented edit distance (TD) and token oriented length of longest common subsequence (TLCSLen) is calculated; secondly, considering the TD and TLCSLen, a similarity calculation formula is given to measure similarity of Source code; Thirdly, a dynamic and variable similarity threshold is set to determine whether there is plagiarism between Source codes, which ensure a relatively reasonable judgment of plagiarism. This method has been applied to the university's programming course work online submission system and online examination system. Practical application results show that this method can identify similar Source code timely, effectively and accurately.

Multi-Agent based Sequence Algorithm for Detecting Plagiarism and Clones in Java Source Code using Abstract Syntax Tree

Plagiarism and clone detection plays an important role in software security protection, software maintenance and license issues. Source-code similarity detection method can be classified as string-based, token-based, parse-tree-based and program-dependency-based. All of these approaches have certain limitations and can not meet the requirements when the source code is large and may produce false positives. But, parse-tree based detection improves the detection ability and efficiency. This paper describes method and statement based source code similarity detection, which detects the simple plagiarized code like exact match, near exact match and longest common sequence using multi-agent based detection which will perform the detection automatically. Automatic plagiarism detection will be helpful for code clone detection in software industry and plagiarism detection in projects.

A Source Code Similarity System for Plagiarism Detection

Source code plagiarism is an easy to do task, but very difficult to detect without proper tool support. Various source code similarity detection systems have been developed to help detect source code plagiarism. Those systems need to recognize a number of lexical and structural source code modifications. For example, by some structural modifications (e.g. modification of control structures, modification of data structures or structural redesign of source code) the source code can be changed in such a way that it almost looks genuine. Most of the existing source code similarity detection systems can be confused when these structural modifications have been applied to the original source code. To be considered effective, a source code similarity detection system must address these issues. To address them, we designed and developed the source code similarity system for plagiarism detection. To demonstrate that the proposed system has the desired effectiveness, we performed a well-known conformism test. The proposed system showed promising results as compared with the JPlag system in detecting source code similarity when various lexical or structural modifications are applied to plagiarized code. As a confirmation of these results, an independent samples t-test revealed that there was a statistically significant difference between average values of F-measures for the test sets that we used and for the experiments that we have done in the practically usable range of cut-off threshold values of 35–70%.