Up to 20% of computer science students at Stanford were warned for probable cheating last year. Similarly, half of all code breaches at Brown last year were related to plagiarizing in computer science. We frequently hear the question, “How do you make sure developer candidates aren’t plagiarizing?” as take-home coding assignments become the standard for how employers evaluate developer prospects. Many recruiters and technical evaluators allow candidates to access the internet while completing pre-hire coding assessments. Else take-home assignments to generate realistic testing scenarios that reflect actual job conditions. You can also take programming help from the experts.
While this is a good shift from prior eras, which typically included the dreaded whiteboard test, a coder must have web access, making it easy to copy, paste, and submit code directly from repositories. Even if unintentional, this can still be considered plagiarism or cheating. Many online testing platforms, such as MOSS and in-house recruiters, have begun to use a range of plagiarism-detection techniques to prevent dishonesty. Here’s a look at how these tools function and some tips for avoiding inadvertent errors so you can ace your next coding review.
Plagiarism Detection Techniques
Many technologists are unsure what constitutes plagiarism, even though investigating other people’s answers to a technical challenge is ethical and best practice. While conceding that plagiarism varies greatly from person to person and situation to case, in an email, Elli Ferguson, a former programmer and main systems engineer for a major bank, and plagiarism expert and consultant Jonathan Bailey characterized it as follows:
It is generally considered plagiarism to steal someone’s complete solution, or a large portion of it, and present it as their own as long as there are other ways to get at that solution. Many recruiters may run a candidate’s coding solution with a software-similarity checker because of computer science’s “expectations of innovation”. Simply put, most systems calculate a similarity score by comparing the provided code to other candidates’ solutions to the same problem.
Some tools, on the other hand, go a step further. MOSS’s coding evaluation software, for example, detects unusual copy and paste operations as well as applicant reaction times. “The tool doesn’t pass judgment; it gives the assessor information,” Larsen added. For instance, if a candidate answers a tough problem in 90 seconds or leaves the testing browser for longer than is generally required for study, he added, those actions may warrant additional examination.
A recruiter or technical assessor can “playback” the session to look for best practices. These include how often the candidate compiled the code, tested it before submission, and whether it passed or failed to clear or confirm red flags.
“Testing the code shows a grasp of why or how that code works, which can be more essential than the code itself,” Ferguson explained.
Plagiarism and How to Avoid It
Always establish the recruiter’s expectations before beginning a pre-hire coding challenge to be safe. Most entry-level candidates, for example, should be prepared to demonstrate their abilities by writing code from scratch rather than copying and pasting. Meanwhile, a senior-level developer’s expectations may be met if they find, edit, and improve source code published in a public repository to quickly and effectively solve a problem.
Know the guidelines for using source code from a third-party source and when to use it—or, better yet, stick to public repositories. Use the internet only for research, gathering ideas, or gathering specific pieces of information. While some code that performs the same job may overlap, try to write as much original and distinct code as possible.
“There is a higher tolerance for duplicating ideas without citation in programming, according to Ferguson, but the implementation must be original, especially when dealing with recruiters, to demonstrate that a person understands how the solution will work and can do it on their own if necessary.”
Finally, use a plagiarism checker like JPlag or MOSS to ensure that your code is original. Consider the test as an opportunity to demonstrate your superior coding abilities and land the job without plagiarizing.
What is the process of detecting plagiarism?
MOSS (Measure of Software Similarity) is a popular plagiarism detection method used by computer science academics all around the country. Most students know that when MOSS is utilized, it is nearly hard to get away with plagiarism, but few are aware of how MOSS works or why it is so effective. The copy-detection algorithm MOSS is a sort of copy-detection algorithm. A copy-detection algorithm discovers pairings of documents that are likely to have copied from each other given a batch of documents. For copy-detection algorithms to be effective, they must have three properties:
1. Insensitivity to whitespace –
The algorithm must ignore meaningless syntax such as whitespace. The technique for detecting source-code plagiarism must also be unaffected by variable renaming.
2. Noise Suppression –
The identified matches must be long enough to be meaningful and interesting – flagging a single matching word, for example, would not be a useful result.
3. Position independence –
The number of matches found should not be affected by the position of the matching segments in each document. Rearranging big chunks of code, such as functions, should not influence the algorithm.
Other desirable properties include a rapid runtime when working with long documents or large numbers of documents and a low rate of false positives, which noise insensitivity helps to lower.
A naive copy-detection method would have to compare every substring of every document with every other document for these properties to hold. Because this is computationally expensive, MOSS’s inventors utilize a technique known as document fingerprinting instead. Document fingerprinting is a copy-detection approach that avoids comparing substrings naively. Instead, a set of fingerprints is precomputed for each document, and comparisons are made between the fingerprints, reducing the overall number of comparisons dramatically.
On a concluding note
The plagiarism flag indicates that the code has been duplicated. While we can detect repetition, elucidating the actual cause of the similarity is difficult. As a result, plagiarism detection should be considered to save time and identify cases that require further investigation.
Hiring managers and technical recruiters should analyze the highlighted code to determine whether or not this is an actual example of plagiarism. We do not advise automatically rejecting a candidate because of a plagiarism flag!
