Source Code Similarity Detection Research Articles

A Survey of Binary Code Similarity Detection Techniques

Binary Code Similarity Detection is a method that involves comparing two or more binary code segments to identify their similarities and differences. This technique plays a crucial role in areas such as software security, vulnerability detection, and software composition analysis. With the extensive use of binary code in software development and system optimization, binary code similarity detection has become an important area of research. Traditional methods of source code similarity detection face challenges when dealing with the unreadable and complex nature of binary code, necessitating specialized techniques and algorithms. This review compares and summarizes various techniques and methods of binary code similarity detection, highlighting their strengths and limitations in handling different characteristics of binary code. Additionally, the article suggests potential future research directions. As research and innovation in this technology continue to advance, binary code similarity detection is expected to play an increasingly significant role in fields like software security.

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

SummaryMassive‐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.

Teacher-oriented Source Code Similarity Detection and Visualization for Programming Assignments

In programming classes, instructors need to work with numerous exercise submissions to verify whether the submitted source code meets the requirements, and whether there is any unauthorized borrowing of code fragments.The checking procedure is laborious requiring much unproductive effort and time. However, ignoring instances of potential plagiarism may negatively impact learner motivation. Despite the existence of practical tools developed for software testing and similarity detection, there are still issues in developing an open-source submission assessment system that would streamline the classroom workflow. This paper describes a practical submission assessment system that reduces the time teachers spend checking the solutions submitted by students.

T-norms or t-conorms? How to aggregate similarity degrees for plagiarism detection

Making correct decisions as to whether code chunks should be considered similar becomes increasingly important in software design and education and not only can improve the quality of computer programs, but also help assure the integrity of student assessments. In this paper we test numerous source code similarity detection tools on pairs of code fragments written in the data science-oriented functional programming language R. Contrary to mainstream approaches, instead of considering symmetric measures of “how much code chunks A and B are similar to each other”, we propose and study the nonsymmetric degrees of inclusion “to what extent A is a subset of B” and “to what degree B is included in A”. Overall, t-norms yield better precision (how many suspicious pairs are actually similar), t-conorms maximise recall (how many similar pairs are successfully retrieved), and custom aggregation functions fitted to training data provide a good balance between the two. Also, we find that program dependence graph-based methods tend to outperform those relying on normalised source code text, tokens, and names of functions invoked.

Keyphrase Extraction And Source Code Similarity Detection-A Survey

Keyphrase extraction is the starting phase of feature extraction and many other NLP tasks. Hence, keyphrase extraction ensures the separation of essential and relevant information from the rest of the document or corpus. A similarity score is used for detecting similarities between two or more documents. In this paper, a summary of existing research conducted in the field of keyphrase extraction and source code similarity detection is presented. The literature focuses on more concept-based research instead of going towards the domain level. There exist a promising potential for the creation of recommendation systems by employing similarly situated research techniques.

Flowchart-Based Cross-Language Source Code Similarity Detection

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.

Flow Chart Generation-Based Source Code Similarity Detection Using Process Mining

Source code similarity detection has extensive applications in computer programming teaching and software intellectual property protection. In the teaching of computer programming courses, students may utilize some complex source code obfuscation techniques, e.g., opaque predicates, loop unrolling, and function inlining and outlining, to reduce the similarity between code fragments and avoid the plagiarism detection. Existing source code similarity detection approaches only consider static features of source code, making it difficult to cope with more complex code obfuscation techniques. In this paper, we propose a novel source code similarity detection approach by considering the dynamic features at runtime of source code using process mining. More specifically, given two pieces of source code, their running logs are obtained by source code instrumentation and execution. Next, process mining is used to obtain the flow charts of the two pieces of source code by analyzing their collected running logs. Finally, similarity of the two pieces of source code is measured by computing the similarity of these two flow charts. Experimental results show that the proposed approach can deal with more complex obfuscation techniques including opaque predicates and loop unrolling as well as function inlining and outlining, which cannot be handled by existing work properly. Therefore, we argue that our approach can defeat commonly used code obfuscation techniques more effectively for source code similarity detection than the existing state-of-the-art approaches.

Particle swarm optimisation-based source code plagiarism detection approach using non-negative matrix factorisation algorithm

Source code plagiarism is easy to do the 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. Numerous efforts have been made in the literature to introduce an efficient source code detection approach with less time complexity and accurate classification of plagiarised codes. However, there exists a tradeoff amongst the less complexity and high accuracy. In a similar way, this paper likewise attempted to build a framework to detect the plagiarised codes from the source code corpus. This approach employed an intelligent swarm optimisation algorithm known as PSO in the detection phase and robust matrix factorisation algorithm known as non-negative matrix factorisation based on alternative least square (ALS) algorithm for reduction of features from the sparse matrix. Depending on the implementation, ALS is very fast and significantly less work than an SVD implementation. The experimental results showed that it has good performance compared to the other existing approaches such as precision and recall.

Particle swarm optimisation-based source code plagiarism detection approach using non-negative matrix factorisation algorithm

Source code plagiarism is easy to do the 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. Numerous efforts have been made in the literature to introduce an efficient source code detection approach with less time complexity and accurate classification of plagiarised codes. However, there exists a tradeoff amongst the less complexity and high accuracy. In a similar way, this paper likewise attempted to build a framework to detect the plagiarised codes from the source code corpus. This approach employed an intelligent swarm optimisation algorithm known as PSO in the detection phase and robust matrix factorisation algorithm known as non-negative matrix factorisation based on alternative least square (ALS) algorithm for reduction of features from the sparse matrix. Depending on the implementation, ALS is very fast and significantly less work than an SVD implementation. The experimental results showed that it has good performance compared to the other existing approaches such as precision and recall.

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.

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%.