This paper presents SPOON, a library for the analysis and transformation of Java source code. SPOON enables Java developers to write a large range of domain-specific analyses and transformations in an easy and concise manner. SPOON analyses and transformations are written in plain Java. With SPOON, developers do not need to dive into parsing, to hack a compiler infrastructure, or to master a new formalism. Copyright © 2015 John Wiley & Sons, Ltd.

/pdf/spoon-a-library-for-implementing-analyses-and-393587i6x4.pdf

SPOON: A library for implementing analyses and transformations of Java source code

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.

/pdf/a-comparison-of-code-similarity-analysers-a6e0bori24.pdf

A comparison of code similarity analysers

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%.

A Source Code Similarity System for Plagiarism Detection

Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This article focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness. While writing the review, some interesting insights have been found about metrics and datasets for quantitative tool comparison and categorisation of detection algorithms. Also, existing obfuscation methods classifications have been expanded together with a new definition of “source-code plagiarism detection in academia.”

Source-code Similarity Detection and Detection Tools Used in Academia: A Systematic Review

In this paper we have done an overview of eective plagiarism detection methods that have been used for natural language text plagia- rism detection, external plagiarism detection, clustering-base plagiarism detection and some methods used in code source plagiarism detection, also we have done a comparison between five of software used for tex- tual plagiarism detection: (PlagAware, PlagScan, Check for Plagiarism, iThenticate and PlagiarismDetection.org), software are compared with respect of their features and performance.

/pdf/overview-and-comparison-of-plagiarism-detection-tools-1a2u0h85m1.pdf

Overview and Comparison of Plagiarism Detection Tools

In this paper we compare five tools for detecting plagiarism in source code texts: JPlag, Marble, moss, Plaggie, and sim. The tools are compared with respect to their features and performance. For the performance comparison we carried out two experiments: to compare the sensitivity of the tools for different plagiarism techniques we have applied the tools to a set of intentionally plagiarised programs. To get a picture of the precision of the tools, we have run the tools on several incarnations of a student assignment and compared the top 10’s of the results.

/pdf/a-comparison-of-plagiarism-detection-tools-9uzbv71mw1.pdf

A comparison of plagiarism detection tools

In this paper we compare five tools for detecting plagiarism in Java source code texts: JPlag, Marble, moss, Plaggie, and sim. The tools are compared with respect to their features and performance. For the performance comparison we carried out two experiments: to compare the sensitivity of the tools for different plagiarism techniques we have applied the tools to a set of intentionally plagiarised programs. To get a picture of the precision of the tools, we have run the tools on several incarnations of a student assignment and compared the top 10's of the results.

Plagiarism detection for Java: a tool comparison

When analyzing software systems we face the challenge of how to implement a particular analysis for different programming languages. A solution for this problem is to write a single analysis using a code query language, abstracting from the specificities of languages being analyzed. Over the past ten years many code query technologies have been developed, based on different formalisms. Each technology comes with its own query language and set of features. To determine the state of the art of code querying we compare the languages and tools for seven code query technologies: Grok, Rscript, JRelCal, Semmle Code, JGraLab, CrocoPat and JTransformer. The specification of a package stability metric is used as a running example to compare the languages. The comparison involves twelve criteria, some of which are concerned with properties of the query language (paradigm, types, parametrization, polymorphism, modularity, and libraries), and some of which are concerned with the tool itself (output formats, interactive interface, API support, interchange formats, extraction support, and licensing). We contextualize the criteria in two usage scenarios: interactive and tool integration. We conclude that there is no particularly weak or dominant tool. As important improvement points, we identify the lack of library mechanisms, interchange formats, and possibilities for integration with source code extractors.

/pdf/a-comparative-study-of-code-query-technologies-229zgc0yk4.pdf

Peter Rademaker

Papers

A comparison of plagiarism detection tools

Plagiarism detection for Java: a tool comparison

A Comparative Study of Code Query Technologies