Tuesday, June 4, 2019

Code-based Plagiarism Detection Techniques

Code-establish Plagiarism Detection TechniquesBiraj Upadhyaya and Dr. Samarjeet BorahAbstract- The copying of programming assignments by students specially at the undergraduate as well as graduate(prenominal) level is a common practice. Efficient mechanisms for celebrateing plagiarised code is therefore needed. Text based plagiarization detection techniques do not buy the farm well with source codes. In this paper we are going to analyse a code- based buccaneering detection technique which is employed by dissimilar plagiarism detection tools like JPlag, MOSS, CodeMatch etc.IntroductionThe word Plagiarism is derived from the Latin word plagiarie which means to kidnap or to abduct. In academicia or pains plagiarism refers to the act of copying materials without actually acknowledging the original source1. Plagiarism is considered as an ethical offence which may incur serious disciplinary actions such as sharp reduction in marks and even expulsion from the university in severe baptisterys. Student plagiarism primarily falls into two categories textbook-based plagiarism and code-based plagiarism. Instances of text based plagiarism includes word to word copy, paraphrasing, plagiarism of secondary sources, plagiarism of ideas, plagiarism of secondary sources, plagiarism of ideas, blunt plagiarism or authorship plagiarism etc. Plagiarism is considered code based when a student copies or modifies a program required to be submitted for a programming assignment. Code based plagiarism includes verbatim copying, changing comments, changing white space and corpseatting, renaming identifiers, reordering code blocks, changing the order of operators/ operands in expression, changing data types, adding redundant contention or variables, replacing control structures with equivalent structures etc2.BackgroundText based plagiarism detection techniques do not work well with a coded stimulant drug or a program. Experiments have suggested that text based systems ignore c oding syntax, an indispensable part of any programming construct thus posing a serious drawback. To overcome this problem code-based plagiarism detection techniques were developed. Code-based plagiarism detection techniques can be classified into two categories viz. Attributed oriented plagiarism detection and Structure oriented plagiarism detection.Attribute oriented plagiarism detection systems measure properties of assignment submissions3. The followers attributes are consideredNumber of unusual operatorsNumber of unique operandsTotal number of occurrences of operatorsTotal number of occurrences of operands base on the above attributes, the degree of similarity of two programs can be considered.Structure oriented plagiarism detection systems deliberately ignore easily modifiable programming elements such as comments, profital white spaces and variable names. This makes this system less susceptible to addition of redundant information as analyzed to attribute oriented plagiari sm detection systems. A student who is aware of this kind of plagiarism detection system cosmos deployed at his institution would rather complete the assignment by himself/herself instead of working on a tedious and time consuming modification task. ascendible Plagiarism DetectionSteven Burrows in his paper Efficient and utile Plagiarism Detection for Large Code Repositories3 provided an algorithm for code -based plagiarism detection. The algorithm comprises of the following stepsTokenization look 1.0Let us consider a simple C programinclude int main( ) int var for (var=0 var printf(%dn, var) return 0Table 1.0 Token list for program in Figure 1.0.Here ALPHANAME refers to any function name, variable name or variable value. STRING refers to range of a function enclosed character(s).The corresponding keepsake stream for the program in Figure 1.0 is given asSNABjSNRANKNNJNNDDBjNA5ENBlgNlNow the above token is converted to N-gram representation. In our case the value of N is chosen as 4. The corresponding tokenization of the above token stream is shown underSNAB NABj ABjS BjSN jSNR SNRA NRAN RANK ANKN NKNN KNNJ NNJN NJNN JNND NNDD NDDB DDBj DBjN BjNA jNA5 NA5E A5EN 5ENB ENBl NBlg BlgN lgNlThese 4-grams are generated using the slip window technique. The sliding window technique generates N-grams by moving a window of size N across all parts of the string from left to right of the token stream.The use of N-grams is an appropriate method of performing structural plagiarism detection because any change to the source code will only affect a few neighbouring N-grams. The modified version of the program will have a large percentage of unchanged N-grams, hence it will be easy to detect plagiarism in this program .Index ConstructionThe second step is to create an inverted index of these N-grams . An inverted index consists of a lexicon and an inverted list. It is shown belowTable 2.0 Inverted IndexReferring to above inverted index for mango, we can conclude that man go occurs in three documents in the collection. It occurs once in document no. 31, thrice in document no. 33 and twice in document no. 15. Similarly we can represent our 4-gram representation of Figure 1.0 with the help of an inverted index. The inverted index for any five 4-grams is shown below in Table 3.0.Table 3.0 Inverted IndexQueryingThe next step is to ask the index. It is understandable that each motion is an N-gram representation of a program. For a token stream of t tokens, we require (t n + 1) N-grams where n is the length of the N-gram . Each query returns the ten most similar programs matching the query program and these are organised from most similar to least similar. If the query program is one of the indexed programs, we would foretell this result to produce the highest score. We assign a similarity score of 100% to the exact or top match3. All other programs are given a similarity score relative to the top score .Burrows experiment compared against an index of 296 programs shown in Table 4.0 presents the top ten results of one N-gram program file (0020.c). In this example, it is seen that the file scored against itself generates the highest relative score of 100.00%. This score is ignored, but it is used to generate a relative similarity score for all other results. We can similarly see that the program 0103.c is very similar to program 0020.c with a score of 93.34% .Rank Query Index Raw SimilarityFile File patsy ScoreTable 4.0 Results of the program 0020.c compared to an index of 296 programs.Comparison of various Plagiarism Detection Tools4.1 JPlag The salient features of this tool are presented belowJPlag was developed in 1996 by Guido MalpohlIt currently supports C, C++, C, Java, Scheme and natural language textIt is a free plagiarism detection toolIt is use to detect bundle plagiarism among multiple set of source code files.JPlag uses Greedy String Tiling algorithm which produces matches ranked by average and maximum similarity.I t is used to compare programs which have a large variation in size which is probably the result of inserting a dead code into the program to disguise the origin.Obtained results are displayed as a set of HTML pages in a form of a histogram which presents the statistics for analyzed filesCodeMatchThe salient features of this tool are presented belowIt was developed by in 2003 by Bob Zeidman and under the licence of SAFE CorporationThis program is available as a standalone application.It supports 26 different programming languages including C, C++, C, Delphi, Flash ActionScript, Java, JavaScript, SQL etcIt has a free version which allows only one trial comparison where the total of all files being examined doesnt exceed the amount of 1 megabyte of dataIt is mostly used as forensic software in copyright infringement casesIt determines the most highly correlated files placed in multiple directories and subdirectories by comparing their source code .Four types of matching algorithms are used Statement duplicate, Comment Matching, Instruction Sequence Matching and Identifier Matching .The results come in a form of HTML basic report that lists the most highly correlated pairs of files.MOSSThe salient features of this plagiarism detection tool are as followsThe full form of MOSS is Measure of Software SimilarityIt was developed by Alex Aiken in 1994It is provided as a free Internet service hosted by Stanford University and it can be used only if a user creates an accountThe program can analyze source code written in 26 programming languages including C, C++, Java, C, Python, Pascal, Visual Basic, Perl etc.Files are submitted through the command line and the processing is performed on the Internet serverThe current form of a program is available only for the UNIX platformsMOSS uses Winnowing algorithm based on code-sequence matching and it analyses the syntax or the structure of the observed filesMOSS maintains a database that stores an ingrained representation of pr ograms and then looks for similarities between themComparative Analysis TableConclusionIn this paper we learnt a structured code-based plagiarism technique known as Scalable Plagiarism Detection. Various processes like tokenization, indexing and query-indexing were also studied. We also studied various salient features of various code-based plagiarism detection tools like JPlag, CodeMatch and MOSS.ReferencesGerry McAllister, Karen Fraser, Anne Morris, Stephen Hagen, hazelnut tree White http//www.ics.heacademy.ac.uk/resources/assessment/plagiarism/Georgina Cosma , An Approach to Source-Code Plagiarism Detection and Investigation Using Latent Semantic Analysis , University of Warwick, Department of Computer Science, July 2008Steven Burrows, Efficient and Effective Plagiarism Detection for Large Code Repositories, School of Computer Science and Information Technology , Melbourne, Australia, October 2004Vedran Juric, Tereza Juric and Marija Tkalec ,Performance Evaluation of Plagiarism Detection Method Based on the Intermediate Language , University of Zagreb

No comments:

Post a Comment

Note: Only a member of this blog may post a comment.