To this date, all methodologies for detecting plagiarism  have focused on investigating the final digital "outcome", eg. a document or source code. Our novel approach takes the creation process into account using logged events  . We look at interaction logs of an author with the software used for creation of the work. Detection relies on comparing histograms of command usages of multiple logs . A work is classified as plagiarism , if its log deviates too much from logs of "honestly created" works or if its log is too similar to another log. The technique supports detecting plagiarism for digital outcomes stemming from unique taskssuch as thesis as well as equal\emph{ \emph{ tasks such as assignments where the same problem sets are solved by many students. Evaluation focuses on the latter case using collected logs by an interactive development environment (IDE) from more than 60 students for three programming assignments.
All methodologies for detecting plagiarism to date have focused on the final digital "outcome", such as a document or source code. Our novel approach takes the creation process into account using logged events collected by special software or by the macro recorders found in most office applications . We look at an author's interaction logs with the software used to create the work. Detection relies on comparing the histograms of multiple logs' command use . A work is classified as plagiarism if its log deviates too much from logs of "honestly created" works or if its log is too similar to another log. The technique supports the detection of plagiarism for digital outcomes that stem from\emph{unique tasks, such as theses and\emph{equal tasks such as assignments for which the same problem sets are solved by multiple students. Focusing on the latter case , we evaluate this approach using logs collected by an interactive development environment (IDE) from more than sixty students who completed three programming assignments.
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To this date, all
All
A
to date
D
investigating
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eg.
such as
A
collected by special software or by the macro recorders found in most office applications
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interaction logs of an author
an author's interaction logs
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for creation of
to create
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histograms of command usages of multiple logs
the histograms of multiple logs' command use
D
,
R
detecting
the detection of
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stemming from unique taskssuch as thesis as well as equal
that stem from
A
unique
A
tasks, such as theses and
A
equal
R
where
for which
R
many students. Evaluation focuses
multiple students. Focusing
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using collected logs
, we evaluate this approach using logs collected
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60 students for
sixty students who completed