Plagiarism detection in students’ programming assignments based on semantics: multimedia e-learning based smart assessment methodology

2020

csci

Several computing courses allow students to choose which programming language they want to use for completing a programming task. This can lead to cross-language code plagiarism and collusion, in which the copied code file is rewritten in another programming language. In response to that, this paper proposes a detection technique which is able to accurately compare code files written in various programming languages, but with limited effort in accommodating such languages at development stage. The only language-dependent feature used in the technique is source code tokeniser and no code conversion is applied. The impact of coincidental similarity is reduced by applying a TF-IDF inspired weighting, in which rare matches are prioritised. Our evaluation shows that the technique outperforms common techniques in academia for handling language conversion disguises. Further, it is comparable to those techniques when dealing with conventional disguises.

Section: Related Workmentioning

confidence: 86%

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

2020

csci

“…Source code token fragments can be compared by adopting techniques from other domains. Works in [11][12][13], for instances, rely on Information Retrieval techniques such as Latent Semantic Indexing [14]. Other examples are works in [15,16] that rely on a clustering technique (where the fragments act as vectors) and a work in [17] that relies on a technique from Information Theory.…”

Section: Related Workmentioning

confidence: 99%

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

Budiman

2019

Computers

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.

“…Some kinds of such measurement are adapted from other domain. For instance, works in and utilize Latent Semantic Analysis from Information Retrieval; works in and utilize similarity measurement from Clustering technique; and a work in utilizes Kolmogorov complexity from Information Theory.…”

Section: Related Workmentioning

confidence: 99%

ES‐Plag: Efficient and sensitive source code plagiarism detection tool for academic environment

Sulistiani

Comp Applic In Engineering

2018

Source code plagiarism detection using Running‐Karp‐Rabin Greedy‐String‐Tiling (RKRGST) is a common practice in academic environment. However, such approach is time‐inefficient (due to RKRGST's cubic time complexity) and insensitive (toward token subsequence rearrangement). This paper proposes ES‐Plag, a plagiarism detection tool featured with cosine‐based filtering and penalty mechanism to handle aforementioned issues. Cosine‐based filtering mitigates time‐inefficiency by excluding non‐potential pairs from RKRGST comparison; while penalty mechanism mitigates insensitivity by reducing the number of matched tokens with the number of matched subsequences prior similarity normalization. In addition to issue‐solving features, ES‐Plag is also featured with project‐based input, colorized adjacency similarity matrix, matched token highlighting, and various similarity algorithms (e.g., Cosine Similarity and Local Alignment). Three findings can be deducted from our evaluation. First, cosine‐based filtering boosts up time efficiency with a trade‐off in effectiveness. Second, penalty mechanism enhances sensitivity even though its improvement in terms of effectiveness is quite limited. Third, ES‐Plag's features are beneficial for examiners.