Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity | Paper Citation Record

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This paper cites Gpt-4 technical report.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Gpt-4 technical report

Reference 1

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This paper cites Moss software plagiarism detector website, 7 2022.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Moss software plagiarism detector website, 7 2022

Reference 2

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This paper cites 246 reasons to cheat: An analysis of students’ reasons for seeking to outsource academic work.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity 246 reasons to cheat: An analysis of students’ reasons for seeking to outsource academic work

Reference 3

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This paper cites Security Engineering: A Guide to Building Dependable Distributed Systems, Third Edition.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Security Engineering: A Guide to Building Dependable Distributed Systems, Third Edition

Reference 4

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This paper cites Source code plagiarism detection in an educational context: A literature mapping.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Source code plagiarism detection in an educational context: A literature mapping

Reference 5

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This paper cites Fooling moss detection with pretrained lan- guage models.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Fooling moss detection with pretrained lan- guage models

Reference 6

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This paper cites Braumoeller and Brian J.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Braumoeller and Brian J

Reference 7

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This paper cites Preventing Automatic Code Plagiarism Generation Through Token String Normalization.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Preventing Automatic Code Plagiarism Generation Through Token String Normalization

Reference 8

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This paper cites Richards Adrion, Betsy Bizot, Susan Davidson, Mary Hall, Susanne Hambrusch, Ellen Walker, and Stuart Zweben.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Richards Adrion, Betsy Bizot, Susan Davidson, Mary Hall, Susanne Hambrusch, Ellen Walker, and Stuart Zweben

Reference 9

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This paper cites Lewis, Matthew West, and Craig Zilles.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Lewis, Matthew West, and Craig Zilles

Reference 10

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This paper cites How far are we? the triumphs and trials of generative ai in learning software engi- neering.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity How far are we? the triumphs and trials of generative ai in learning software engi- neering

Reference 11

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This paper cites Dominance statistics: Ordinal analyses to answer ordinal questions.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Dominance statistics: Ordinal analyses to answer ordinal questions

Reference 13

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Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Statistical Power Analysis for the Behavioral Sciences

Reference 14

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This paper cites Towards a definition of source-code plagia- rism.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Towards a definition of source-code plagia- rism

Reference 15

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This paper cites Plagiarism issues for higher education.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Plagiarism issues for higher education

Reference 16

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This paper cites How chatgpt will change software engineering ed- ucation.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity How chatgpt will change software engineering ed- ucation

Reference 17

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Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Unresolved cited work

Reference 18

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This paper cites Semantic similarity search for source code plagiarism detection: An exploratory study.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Semantic similarity search for source code plagiarism detection: An exploratory study

Reference 19

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This paper cites Faidhi and S.K.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Faidhi and S.K

Reference 20

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This paper cites Detecting machine-obfuscated plagiarism.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Detecting machine-obfuscated plagiarism

Reference 21

Resolution: verified exact; doi, observed 2026-08-07T14:04:53.471864Z

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This paper cites Sim: a utility for detecting similarity in com- puter programs.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Sim: a utility for detecting similarity in com- puter programs

Reference 22

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This paper cites Grissom and John J.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Grissom and John J

Reference 23

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This paper cites Evading watermark based detection of ai-generated content.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Evading watermark based detection of ai-generated content

Reference 24

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This paper cites Plagiarism in programming assignments.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Plagiarism in programming assignments

Reference 25

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This paper cites Do code clones matter? In Proceedings of the 31st International Conference on Software Engineering, ICSE ’09, pages 485–495, USA, 1 2009.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Do code clones matter? In Proceedings of the 31st International Conference on Software Engineering, ICSE ’09, pages 485–495, USA, 1 2009

Reference 26

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This paper cites Detecting source code plagiarism on introductory programming course assignments using a bytecode approach.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Detecting source code plagiarism on introductory programming course assignments using a bytecode approach

Reference 27

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This paper cites Similarities of human and ai assistance in programming plagiarism: Student perspective.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Similarities of human and ai assistance in programming plagiarism: Student perspective

Reference 28

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This paper cites Preprocessing for source code similarity detection in introductory programming.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Preprocessing for source code similarity detection in introductory programming

Reference 29

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Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Educating students about programming plagiarism and collusion via formative feedback.ACM Trans

Reference 30

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This paper cites Detecting ai assisted submissions in introductory programming via code anomaly.Education and Information Technologies, 29(13):16841–16866, 9 2024.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Detecting ai assisted submissions in introductory programming via code anomaly.Education and Information Technologies, 29(13):16841–16866, 9 2024

Reference 31

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This paper cites Will ChatGPT get you caught? Rethinking of Plagiarism Detection.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Will ChatGPT get you caught? Rethinking of Plagiarism Detection

Reference 32

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This paper cites Automatic source code plagiarism detection.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Automatic source code plagiarism detection

Reference 33

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This paper cites Artificial intelligence, text generation tools and chatgpt – does digital watermarking offer a solution? International Journal for Educational Integrity , 19(1), 7 2023.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Artificial intelligence, text generation tools and chatgpt – does digital watermarking offer a solution? International Journal for Educational Integrity , 19(1), 7 2023

Reference 34

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This paper cites Educating computer programming students about plagiarism through use of a code similarity detection tool.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Educating computer programming students about plagiarism through use of a code similarity detection tool

Reference 35

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Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Validating and restoring defense in depth using attack graphs

Reference 36

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This paper cites Plagiarism detection in computer programming using feature extraction from ultra-fine-grained repositories.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Plagiarism detection in computer programming using feature extraction from ultra-fine-grained repositories

Reference 37

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This paper cites Semantics-based obfuscation-resilient binary code similarity comparison with applications to software and algorithm plagiarism detection.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Semantics-based obfuscation-resilient binary code similarity comparison with applications to software and algorithm plagiarism detection

Reference 38

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This paper cites Dolos: Language-agnostic plagiarism detection in source code.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Dolos: Language-agnostic plagiarism detection in source code

Reference 39

Resolution: verified exact; doi, observed 2026-08-07T14:04:52.053494Z

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Observation f7e8e014-a350-4aa6-a1b8-ef9c058c6725 · outbound

This paper cites Preventing Refactoring Attacks on Software Plagiarism Detec- tion through Graph-Based Structural Normalization.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Preventing Refactoring Attacks on Software Plagiarism Detec- tion through Graph-Based Structural Normalization

Reference 40

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This paper cites Audentes: Automatic detection of tentative plagiarism according to a reference solution.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Audentes: Automatic detection of tentative plagiarism according to a reference solution

Reference 41

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Observation 3ea4299e-e0fc-443f-a731-aad89331bef1 · outbound

This paper cites Cheating in computer science.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Cheating in computer science

Reference 42

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This paper cites Syntax- based improvements to plagiarism detectors and their evaluations.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Syntax- based improvements to plagiarism detectors and their evaluations

Reference 43

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Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Effect of source-code preprocessing techniques on plagiarism detection accuracy in student programming assignments

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:01.645557Z

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Observation c47c5964-0536-4e3c-9d17-5c8f129936cd · outbound

This paper cites Source-code similarity detection and detection tools used in academia: A systematic review.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Source-code similarity detection and detection tools used in academia: A systematic review

Reference 45

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Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Symbols of One-Loop Integrals From Mixed Tate Motives

Reference 46

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Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Unresolved cited work

Reference 47

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This paper cites Progpedia: Collection of source-code submitted to introductory programming assignments.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Progpedia: Collection of source-code submitted to introductory programming assignments

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:47.813189Z

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Observation 56ce2070-9067-402d-9645-89f95e159701 · outbound

This paper cites In other (people’s) words: Plagiarism by university students–literature and lessons.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity In other (people’s) words: Plagiarism by university students–literature and lessons

Reference 50

Resolution: verified exact; doi, observed 2026-08-07T14:04:51.811775Z

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Observation 7806cb6a-4972-4333-b2f7-b2b58d0f0368 · outbound

This paper cites Benefits and drawbacks of source code plagiarism detection in engineering education.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Benefits and drawbacks of source code plagiarism detection in engineering education

Reference 51

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:04:55.750077Z

Source-reported events for the cited work

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Observation aaad2644-5147-44c9-bfd2-331d2a7276e9 · outbound

This paper cites Spoon: Program Analysis and Transformation in Java.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Spoon: Program Analysis and Transformation in Java

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:01.489721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8e01608b-ea66-492d-b443-205dbe3f3551 · outbound

This paper cites JPlag: Finding pla- giarisms among a set of programs.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity JPlag: Finding pla- giarisms among a set of programs

Reference 53

Resolution: verified exact; doi, observed 2026-08-07T14:04:51.556600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b4e2865-d1cb-4a8d-a5f0-13465aa04aa0 · outbound

This paper cites Finding plagiarisms among a set of programs with jplag.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Finding plagiarisms among a set of programs with jplag

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:01.162531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 553b93c2-7750-47ae-ae88-c0293deea78c · outbound

This paper cites an unresolved cited work.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Unresolved cited work

Reference 55

Resolution: verified exact; doi, observed 2026-08-07T14:04:51.344809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2fb70fa1-d41a-4549-9727-0e59b7a9a326 · outbound

This paper cites Romano, J.D.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Romano, J.D

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:00.863569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9d51457d-646c-4c72-9f3c-163614f26256 · outbound

This paper cites Roy, James R.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Roy, James R

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:48.601556Z

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Observation 0548091d-0df8-4600-8fde-03e9c03fd719 · outbound

This paper cites Guidelines for conducting and reporting case study research in software engineering.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Guidelines for conducting and reporting case study research in software engineering

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:00.588285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 161c24c4-352e-4cec-a9fa-53ac5aa537c6 · outbound

This paper cites Replication Package for ”Evaluating Software Plagiarism Detection in the Age of AI”, 5 2025.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Replication Package for ”Evaluating Software Plagiarism Detection in the Age of AI”, 5 2025

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:00.433253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7645cbee-b489-4a4f-b5a7-58d7813f02d1 · outbound

This paper cites Detect- ing automatic software plagiarism via token sequence normalization.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Detect- ing automatic software plagiarism via token sequence normalization

Reference 60

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Source-reported events for the cited work

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Observation 82d3cca3-5186-4fe4-ba86-11fb16380320 · outbound

This paper cites Obfuscation- resilient software plagiarism detection with jplag.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Obfuscation- resilient software plagiarism detection with jplag

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:49.067090Z

Source-reported events for the cited work

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Observation 325d6c09-7743-4069-9024-986d284e105f · outbound

This paper cites Mitigating ob- fuscation attacks on software plagiarism detectors via subsequence merging.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Mitigating ob- fuscation attacks on software plagiarism detectors via subsequence merging

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:49.156414Z

Source-reported events for the cited work

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Observation 19396f6c-843f-4b33-a911-4f558412b738 · outbound

This paper cites Detecting modelling plagiarism: Navigating automated obfuscation and generative ai.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Detecting modelling plagiarism: Navigating automated obfuscation and generative ai

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:00.169361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30c91bfd-6956-41ab-8661-0d1cee3a86ef · outbound

This paper cites Negotiating the maze of academic in- tegrity in computing education.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Negotiating the maze of academic in- tegrity in computing education

Reference 65

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:04:55.287360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6cdc061d-833f-4b71-89d5-9ae037c0e3b8 · outbound

This paper cites Ho:YLF amplifier with Ti:Sapphire frontend for pumping mid-infrared optical parametric amplifier.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Ho:YLF amplifier with Ti:Sapphire frontend for pumping mid-infrared optical parametric amplifier

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:49.433575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c01b810-789d-43da-893f-32ada5e6a93d · outbound

This paper cites An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:49.575339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a2c2be7-e810-4b9d-ad6e-ecad4c77f815 · outbound

This paper cites A model for exploring student un- derstandings of plagiarism.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity A model for exploring student un- derstandings of plagiarism

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:04:59.887225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20b5956e-cc57-4106-9114-ae47721f046c · outbound

This paper cites an unresolved cited work.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:49.922163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 812586ba-5d2a-4afc-848f-584c8509bf44 · outbound

This paper cites Plagiarism detectors are a crutch, and a problem.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Plagiarism detectors are a crutch, and a problem

Reference 70

Resolution: verified exact; doi, observed 2026-08-07T14:04:51.069343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a25ac9f9-28ba-48bb-a2e6-d589143bb067 · outbound

This paper cites Testing of detec- tion tools for ai-generated text.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Testing of detec- tion tools for ai-generated text

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:50.169293Z

Source-reported events for the cited work

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Observation a26efb5e-9189-4e5f-b474-6eb1786451ae · outbound

This paper cites Ohlsson, Bj¨ orn Regnell, and An- ders Wessl´ en.Experimentation in Software Engineering.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Ohlsson, Bj¨ orn Regnell, and An- ders Wessl´ en.Experimentation in Software Engineering

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:50.316675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb4f18a5-0db3-4ee7-9c74-a9d25d4f6b56 · outbound

This paper cites Bai, Robert Tairas, and Yu Huang.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Bai, Robert Tairas, and Yu Huang

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:50.435660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38ea78f5-1b38-4ec0-9790-15eb78182aa8 · outbound

This paper cites Tmoss: Using inter- mediate assignment work to understand excessive collaboration in large classes.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Tmoss: Using inter- mediate assignment work to understand excessive collaboration in large classes

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:50.536069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 828394b5-6201-4513-8a7e-53514c04d07a · outbound

This paper cites Toward Obfuscation-resilient Plagiarism Detection.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Toward Obfuscation-resilient Plagiarism Detection

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:04:59.591031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 21d45435-1a9c-4cb8-a48d-aa535d02b603 · outbound

This paper cites Provable Robust Watermarking for AI-Generated Text.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Provable Robust Watermarking for AI-Generated Text

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:50.741881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 029bad75-97fa-4a68-9e5f-c6f3acec3c8f · outbound

This paper cites doi:10.4324/9780203771587.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity doi:10.4324/9780203771587

Reference 1988

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:45.195509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55ce7071-76af-4da6-b279-660a84dffde6 · outbound

This paper cites an unresolved cited work.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity Unresolved cited work

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:48.365844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12fec0d9-1ce8-46b0-8036-968f258f78d9 · outbound

This paper cites doi:10.1007/s10664-008-9102-8.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity doi:10.1007/s10664-008-9102-8

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:48.752884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed5ea978-bc5f-46ec-a75a-2bc4a64ed32f · outbound

This paper cites doi:10.1080/0309877X.2012.706807.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity doi:10.1080/0309877X.2012.706807

Reference 2014

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:04:54.990369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 01d523ac-12d7-44c4-b751-16b6738beb96 · outbound

This paper cites doi:10.1109/ACCESS.2020.2996146.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity doi:10.1109/ACCESS.2020.2996146

Reference 2020

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:04:57.384244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2940a179-dcc9-48a2-8c47-84e0d18b3ff3 · outbound

This paper cites ISBN 9798400700507.

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity ISBN 9798400700507

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:46.013048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers