PasteTrace: A Single Source Plagiarism Detection Tool For Introductory Programming Courses

• URL: http://arxiv.org/abs/2506.17355v1
• Date: Fri, 20 Jun 2025 04:21:07 GMT
• Title: PasteTrace: A Single Source Plagiarism Detection Tool For Introductory Programming Courses
• Authors: Jesse McDonald, Scott Robertson, Anthony Peruma,
• Abstract summary: PasteTrace is a novel open-source plagiarism detection tool designed specifically for introductory programming courses.<n>Unlike traditional methods, PasteTrace operates within an Integrated Development Environment that tracks the student's coding activities in real-time for evidence of plagiarism.
• Score: 4.385741575933952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Introductory Computer Science classes are important for laying the foundation for advanced programming courses. However, students without prior programming experience may find these courses challenging, leading to difficulties in understanding concepts and engaging in academic dishonesty such as plagiarism. While there exists plagiarism detection techniques and tools, not all of them are suitable for academic settings, especially in introductory programming courses. This paper introduces PasteTrace, a novel open-source plagiarism detection tool designed specifically for introductory programming courses. Unlike traditional methods, PasteTrace operates within an Integrated Development Environment that tracks the student's coding activities in real-time for evidence of plagiarism. Our evaluation of PasteTrace in two introductory programming courses demonstrates the tool's ability to provide insights into student behavior and detect various forms of plagiarism, outperforming an existing well-established tool. A video demonstration of PasteTrace and its source code, and case study data are made available at https://doi.org/10.6084/m9.figshare.27115852

Related papers

• The Failure of Plagiarism Detection in Competitive Programming [0.0]
  Plagiarism in programming courses remains a persistent challenge.<n>This paper examines why traditional code plagiarism detection methods frequently fail in competitive programming contexts.<n>We find that widely-used automated similarity checkers can be thwarted by simple code transformations or novel AI-generated code.
  arXiv  Detail & Related papers  (2025-05-13T05:43:49Z)
• Discovering and exploring cases of educational source code plagiarism with Dolos [0.0]
  Dolos is an ecosystem of tools for detecting and preventing plagiarism in educational source code. Educators can now run the entire plagiarism pipeline from a new web app in their browser. New dashboards provide an instant assessment of whether a collection of source files contains suspected cases of plagiarism.
  arXiv  Detail & Related papers  (2024-02-16T17:47:11Z)
• CONCORD: Clone-aware Contrastive Learning for Source Code [64.51161487524436]
  Self-supervised pre-training has gained traction for learning generic code representations valuable for many downstream SE tasks. We argue that it is also essential to factor in how developers code day-to-day for general-purpose representation learning. In particular, we propose CONCORD, a self-supervised, contrastive learning strategy to place benign clones closer in the representation space while moving deviants further apart.
  arXiv  Detail & Related papers  (2023-06-05T20:39:08Z)
• A systematic literature review of capstone courses in software engineering [0.3536605202672354]
  capstone projects are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of software engineering capstone courses presented in the literature.
  arXiv  Detail & Related papers  (2023-01-09T18:04:35Z)
• Giving Feedback on Interactive Student Programs with Meta-Exploration [74.5597783609281]
  Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. Standard approaches require instructors to manually grade student-implemented interactive programs. Online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs.
  arXiv  Detail & Related papers  (2022-11-16T10:00:23Z)
• Measuring Plagiarism in Introductory Programming Course Assignments [0.0]
  This paper discusses the methods of plagiarism and its detection in introductory programming course assignments written in C++. A general framework is developed that uses the three token-based similarity methods as features and predicts if the solution is plagiarized. We achieved an F1 score of 0.955 and 0.971 on original and synthetic datasets.
  arXiv  Detail & Related papers  (2022-04-29T17:06:26Z)
• Neural Language Models are Effective Plagiarists [38.85940137464184]
  We find that a student using GPT-J can complete introductory level programming assignments without triggering suspicion from MOSS. GPT-J was not trained on the problems in question and is not provided with any examples to work from. We conclude that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code.
  arXiv  Detail & Related papers  (2022-01-19T04:00:46Z)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)
• Crop-Transform-Paste: Self-Supervised Learning for Visual Tracking [137.26381337333552]
  In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data. Since the object state is known in all synthesized data, existing deep trackers can be trained in routine ways without human annotation.
  arXiv  Detail & Related papers  (2021-06-21T07:40:34Z)
• Improving Compositionality of Neural Networks by Decoding Representations to Inputs [83.97012077202882]
  We bridge the benefits of traditional and deep learning programs by jointly training a generative model to constrain neural network activations to "decode" back to inputs. We demonstrate applications of decodable representations to out-of-distribution detection, adversarial examples, calibration, and fairness.
  arXiv  Detail & Related papers  (2021-06-01T20:07:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.