We illustrate the state of the art in software plagiarism detection tools by comparing their features and testing them against a wide range of source codes. The source codes were edited according to several types of plagiarism to show the tools accuracy at detecting each type.

The decision to focus our research on plagiarism of programming languages is two fold: on one hand, it is a challenging case-study since programming languages impose a structured writing style; on the other hand, we are looking for the integration of such a tool in an Automatic-Grading System (AGS) developed to support teachers in the context of Programming courses.

Besides the systematic characterisation of the underlying problem domain, the tools were surveyed with the objective of identifying the most successful approach in order to design the aimed plugin for our AGS.

https://drops.dagstuhl.de/opus/volltexte/2014/4566/pdf/14.pdf/

Plagiarism Detection: A Tool Survey and Comparison

Binary code analysis is crucial in various software engineering tasks, such as malware detection, code refactoring, and plagiarism detection. With the rapid growth of software complexity and the increasing number of heterogeneous computing platforms, binary analysis is particularly critical and more important than ever. Traditionally adopted techniques for binary code analysis are facing multiple challenges, such as the need for cross-platform analysis, high scalability and speed, and improved fidelity, to name a few. To meet these challenges, machine learning-based binary code analysis frameworks attract substantial attention due to their automated feature extraction and drastically reduced efforts needed on large-scale programs. In this paper, we provide the taxonomy of machine learning-based binary code analysis, describe the recent advances and key findings on the topic, and discuss the key challenges and opportunities. Finally, we present our thoughts for future directions on this topic.

/pdf/machine-learning-based-analysis-of-program-binaries-a-35gbobnl6j.pdf

Machine Learning-Based Analysis of Program Binaries: A Comprehensive Study

This paper summarizes the goals, organization and results of the first SOCO competitive evaluation campaign for systems that automatically detect the source code re-use phenomenon. The detection of source code re-use is an important research field for both software industry and academia fields. Accordingly, PAN@FIRE track, named SOurce COde Re-use (SOCO) focused on the detection of re-used source codes in C/C++ and Java programming languages. Participant systems were asked to annotate several source codes whether or not they represent cases of source code re-use. In total five teams submitted 17 runs. The training set consisted of annotations made by several experts, a feature which turns the SOCO 2014 collection in a useful data set for future evaluations and, at the same time, it establishes a standard evaluation framework for future research works on the posed shared task.

/pdf/on-the-detection-of-source-code-re-use-3qbd4gmbcy.pdf

On the Detection of SOurce COde Re-use

Use of an Abstract Syntax Tree (AST) to select portions of source code when analyzing the affect of changes in that source code from one version to another. In this way, it is possible to better focus on how changes in the source code, especially changes which only impact limited portion(s) of the source code, affect code quality with respect to measures like code complexity, performance and so on.

Analysis of source code changes

Research on software defect prediction has achieved great success at modeling predictors. To build more accurate predictors, a number of hand-crafted features are proposed, such as static code features, process features, and social network features. Few models, however, consider the semantic and structural features of programs. Understanding the context information of source code files could explain a lot about the cause of defects in software. In this paper, we leverage representation learning for semantic and structural features generation. Specifically, we first extract token vectors of code files based on the Abstract Syntax Trees (ASTs) and then feed the token vectors into Convolutional Neural Network (CNN) to automatically learn semantic features. Meanwhile, we also construct a complex network model based on the dependencies between code files, namely, software network (SN). After that, to learn the structural features, we apply the network embedding method to the resulting SN. Finally, we build a novel software defect prediction model based on the learned semantic and structural features (SDP-S2S). We evaluated our method on 6 projects collected from public PROMISE repositories. The results suggest that the contribution of structural features extracted from software network is prominent, and when combined with semantic features, the results seem to be better. In addition, compared with the traditional hand-crafted features, the F-measure values of SDP-S2S are generally increased, with a maximum growth rate of 99.5%. We also explore the parameter sensitivity in the learning process of semantic and structural features and provide guidance for the optimization of predictors.

/pdf/an-approach-to-semantic-and-structural-features-learning-for-5giduc16on.pdf

An Approach to Semantic and Structural Features Learning for Software Defect Prediction

The code comparison technology plays a very important part in the work of plagiarism detection and software evaluation. Software plagiarism mainly appears as copy-and-paste or with a little modification after this, which will not change the function of the code, such as replacing the name of methods or variables, reordering the sequence of the statements etc. This paper introduces a plagiarism detection tool named CCS (Code Comparison System) which is based on the Abstract Syntax Tree (AST). According to the syntax tree's characteristics, CCS calculates their hash values, transforms their storage forms, and then compares them node by node. As a result, the efficiency improves. Moreover, CCS preprocesses a large amount of source code in its database for potential use, which also accelerate the course of plagiarism detection. CCS also takes special measurement to reduce mistakes when calculating the hash values of the operations like subtraction and division. It performs well in the code comparison field, and is able to help with the copyright protecting of the source code.

Ding Ma

Papers

Code Comparison System based on Abstract Syntax Tree