This document discusses the challenges of defining and identifying plagiarism in programming coursework submissions. It notes that software engineering best practices like code reuse and standard algorithms/patterns can conflict with academic definitions of plagiarism. It also examines ethics issues around methods for identifying plagiarism in code, and recommends as good practice notifying students of potential mini-vivas in advance and giving them access to annotated transcripts before misconduct hearings. The overall aim is to have a fair and balanced approach that considers the complexities of programming assignments and students' perspectives.

1. Musings on
Misconduct
PAULINE BELFORD
MICHAEL JAMES HERON

2. Introduction
 Clashes are often observed between software engineering good practice and
institutional conventions regarding plagiarism.
 Plagiarism in programming is often a result of student misunderstanding
regarding how far good practice extends
 And as such, a degree of empathy is required when assessing and confronting
incidences.
 In this talk, I will discuss the nature of this clash with regards to programming in a
college or university setting.
 I will discuss how plagiarism is commonly identified when assessing coursework
submissions, and the ethical issues raised.
 We conclude the talk with some notes on institutional good practice and how to
remove some of the heat from student interactions.

3. All Programming is Plagiarism?
 Culture of reuse:
 Standardised syntax
 Standard algorithms
 Design patterns
 Architecture is restrictive
 Loops, Selections, Sequential
 Code style is often mandated
 Stylistic elements often inherited
 Reusability and maintainability
 Emphasised as good practice

4. All Programming is Plagiarism?
 We emphasise good practice in software engineering, which is
often at odds with institutional definitions of academic conduct.
 Students can find themselves at odds with their own discipline.
 Reusing their own code (e.g. cross assessment)
 Reusing the code of their colleagues
 Integrating external code into their own work.
 As a discipline, we emphasise that it is important not to reinvent the
wheel.
 And yet, making use of all resources available will likely lead to a clash
with academic norms and expectations.

5. What is Academic Plagiarism?
 Plagiarism implies passing work off as your own without attribution.
 Students are generally au fait with the idea of plagiarism.
 Can recite text book definitions
 Problem is not in understanding, it is in interpretation.
 Often plagiarism represents a lack of awareness that it applies in a given
situation.
 All academics have a responsibility to identify plagiarism.
 Students can receive degree awards for work they did not submit.
 Devalues the qualification for all other students.
 Reflects badly on the institution when student inability is discovered.

6. What is Plagiarism in Programming?
 Almost impossible to define.
 Where does plagiarism live?
 In lines of code?
 In data structures?
 In algorithms?
 In architecture?
 All of these and none of these.
 We exacerbate this problem – we teach plagiarism as good practice.
 Not intentionally, but through a lack of coherent contextualisation.
 Students suffer from our flippancy in teaching these topics without fully covering
the implications for misconduct.
 Often due to time pressure
 Often due to course pressure

7. Methods for identifying plagiarism
in programming
 Can’t be easily automated.
 There is no real Turnitin equivalent for software code. Problem may be
intractable.
 Requires specialist examination of submissions by subject matter
expert.
 Time consuming, prone to mistakes, can’t offer full coverage.
 Attention most directed at obvious candidates for examination.
 But what does obvious mean here?
 Course organisation can frustrate analysis:
 Marking distribution, pair programming submissions, etc.

8. Ethics of Identifying Plagiarism in
Programming
 Directed sampling is ethically questionable.
 Subject to subconscious biases
 Selection bias, etc.
 Focuses attention on those least likely to be successfully hoodwinking..
 In the case of weaker students submitting work beyond their assumed
competence.
 Assumed competence?
 Slanted by familiarity with students within lab situations.
 May miss those students who are quietest.
 Assumed competence comes from our own exposure to evolving student
submissions

9. Ethics of Identifying Plagiarism in
Programming
 Our techniques are ineffective against commissioned work.
 Little success against essay mills
 Class divide?
 Those with the most money are most likely to fly under the radar.
 It is our familiarity with student work that directly informs sampling.
 And this is troublesome.

10. Good practice
 Should inform all students at the beginning of a course that semi-
random subset will be selected for mini-viva.
 Non stigmatising – not only those under suspicion
 Non comprehensive – not all students will be selected.
 Selection criteria for mini-viva is all students under suspicion and a
random sampling of all others.
 Students that are under suspicion are selected for forensic dissection.
 So too is a completely random sampling of all students.
 Each selected student undergoes the same forensic examination of
coursework.
 Same process applied regardless of inclusion criteria.

11. Fair Dealings in Academic
Misconduct
 Many institutions bias academic misconduct hearings against the
student.
 Often unintentionally, and usually without malice.
 Students are often unaware of the charge or evidence until they are
confronted in the meeting.
 This creates unnecessary tensions, stresses on the students, and skews
the outcome.
 It’s unfair to judge a student based on their perceived inability to explain
irregularities under stress in a high-stakes environment.
 We recommend that students see fully annotated transcripts of their
work beforehand, so they can effectively marshal a defence or
explanation.
 Or are aware of the strength of evidence prior to the hearing.

12. Conclusion
 Students often plagiarise not as a result of malice, but instead an
outcome of their lack of specialist knowledge.
 Students often lack the skills to properly evaluate the degree of contribution
they made to a submission.
 To a certain extent, all programming is plagiarism, and we are often
flippant in our treatment of good practice.
 Academics need to be mindful of the fact they play an important role
in helping students interpret plagiarism guidelines within complex
environments.
 In no way are we attempting to minimise the responsibility of the
student in cases of genuine plagiarism.
 We are only attempting to examine and contemplate our own role in the
process.