Selection of code segments for exclusion from code similarity detection

Simon, Oscar Karnalim, Judy Sheard, Ilir Dema, Amey Karkare, Juho Leinonen, Michael Liut, Renee Mccauley

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther


When student programs are compared for similarity, certain segments of code are always sure to be similar. Some of these segments are boilerplate code - public static void main String [] args and the like - and some will be code that was provided to students as part of the assessment specification. The purpose of this working group is to explore what other code is expected to be reasonably common in student assessments, and should therefore be excluded from similarity checking. The answers will clearly vary with programming language, and perhaps with level of assessment item. Working group members will collect assessment submissions from their own or their colleagues' students, and it is hoped that these submissions will together encompass a wide variety of assessment tasks in a wide variety of programming languages. The working group aims to deliver clear guidelines as to what code can reasonably be excluded from automatic code similarity detection in various circumstances. It also aims to deliver a summary of what sort of code lecturers tend to provide for students when setting an assigned task, and why they provide that code.

Original languageEnglish
Title of host publicationProceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education
EditorsAndrew Luxton-Reilly, Monica Divitini
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Electronic)9781450368742
Publication statusPublished - Jun 2020
EventAnnual Conference on Innovation and Technology in Computer Science Education 2020 - Trondheim, Norway
Duration: 15 Jun 202019 Jun 2020
Conference number: 25th (Website) (Proceedings)


ConferenceAnnual Conference on Innovation and Technology in Computer Science Education 2020
Abbreviated titleITiCSE 2020
Internet address


  • academic integrity
  • code similarity detection
  • collusion
  • Plagiarism

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