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@ -242,100 +242,67 @@ Thus, we can discover the number of matching and different paths in the analyzed
\section{Experiments}
% -----------------------------------------
% -----------------------------------------
% -----------------------------------------
% -----------------------------------------
% -----------------------------------------
% -----------------------------------------
% -----------------------------------------
% -----------------------------------------
\section{State of the art}
\subsection{A Subsection Sample}
Please note that the first paragraph of a section or subsection is
not indented. The first paragraph that follows a table, figure,
equation etc. does not need an indent, either.
Subsequent paragraphs, however, are indented.
\subsubsection{Sample Heading (Third Level)} Only two levels of
headings should be numbered. Lower level headings remain unnumbered;
they are formatted as run-in headings.
\paragraph{Sample Heading (Fourth Level)}
The contribution should contain no more than four levels of
headings. Table~\ref{tab1} gives a summary of all heading levels.
% \begin{figure}
% \end{figure}
\begin{table}
\caption{Table captions should be placed above the
tables.}\label{tab1}
\begin{tabular}{|l|l|l|}
\hline
Heading level & Example & Font size and style\\
\hline
Title (centered) & {\Large\bfseries Lecture Notes} & 14 point, bold\\
1st-level heading & {\large\bfseries 1 Introduction} & 12 point, bold\\
2nd-level heading & {\bfseries 2.1 Printing Area} & 10 point, bold\\
3rd-level heading & {\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\
4th-level heading & {\itshape Lowest Level Heading.} Text follows & 10 point, italic\\
\hline
\end{tabular}
We conducted experiments to evaluate the speed of source code analysis. We calculated the results relative to the number of lines of code and files in the project. The main aim of the experiment is to calculate the average number of lines of code and the time to complete the analysis in one minute. This allows us to determine the speed of the algorithm. We used the IntelliJ IDEA Statistic plugin to get the data for the experiment.
Table \ref{tab1} presents the initial data for determining the speed of the proposed algorithm. We selected 10 random Java projects as data.
\begin{table}[]
\caption{Initial data for analyzing the speed of the proposed algorithm.}\label{tab1}
\begin{tabular}{|llll|l|}
\hline
\multicolumn{1}{|l|}{} & \multicolumn{1}{l|}{Project name} & \multicolumn{1}{l|}{Number of lines of code} & Number of java files & Number of rows processed per 1 minute \\ \hline
\multicolumn{1}{|l|}{1} & \multicolumn{1}{l|}{BaseRecycler} & \multicolumn{1}{l|}{3 896} & 92 & 2 491 \\ \hline
\multicolumn{1}{|l|}{2} & \multicolumn{1}{l|}{AlamazDev} & \multicolumn{1}{l|}{15 776} & 103 & 2 658 \\ \hline
\multicolumn{1}{|l|}{3} & \multicolumn{1}{l|}{SnakeBoom} & \multicolumn{1}{l|}{20 534} & 158 & 3 255 \\ \hline
\multicolumn{1}{|l|}{4} & \multicolumn{1}{l|}{retrofit} & \multicolumn{1}{l|}{32 119} & 227 & 2 718 \\ \hline
\multicolumn{1}{|l|}{5} & \multicolumn{1}{l|}{Glide} & \multicolumn{1}{l|}{37 508} & 203 & 2 576 \\ \hline
\multicolumn{1}{|l|}{6} & \multicolumn{1}{l|}{ZXing} & \multicolumn{1}{l|}{51 857} & 310 & 2 533 \\ \hline
\multicolumn{1}{|l|}{7} & \multicolumn{1}{l|}{RxJava} & \multicolumn{1}{l|}{64 101} & 339 & 2 814 \\ \hline
\multicolumn{1}{|l|}{8} & \multicolumn{1}{l|}{VisualProjectCore} & \multicolumn{1}{l|}{71 303} & 450 & 2 969 \\ \hline
\multicolumn{1}{|l|}{9} & \multicolumn{1}{l|}{mc-dev} & \multicolumn{1}{l|}{85 267} & 877 & 2 746 \\ \hline
\multicolumn{1}{|l|}{10} & \multicolumn{1}{l|}{xRayJavaTool} & \multicolumn{1}{l|}{97 249} & 937 & 2 730 \\ \hline
\multicolumn{4}{|l|}{Average value} & 2 750 \\ \hline
\end{tabular}
\end{table}
\begin{itemize}
\item
\end{itemize}
\begin{enumerate}
\item
\end{enumerate}
\noindent Displayed equations are centered and set on a separate
line.
\begin{equation*}
N^{Class} = \lbrace N_{i} \in N | F \left( N_{i}.data \right) = \text{'Class'} \rbrace,
\end{equation*}
Please try to avoid rasterized images for line-art diagrams and
schemas. Whenever possible, use vector graphics instead (see
Fig.~\ref{fig1}).
% \begin{figure}
% \includegraphics[width=\textwidth]{fig1.eps}
% \caption{A figure caption is always placed below the illustration.
% Please note that short captions are centered, while long ones are
% justified by the macro package automatically.} \label{fig1}
% \end{figure}
\begin{theorem}
This is a sample theorem. The run-in heading is set in bold, while
the following text appears in italics. Definitions, lemmas,
propositions, and corollaries are styled the same way.
\end{theorem}
%
% the environments 'definition', 'lemma', 'proposition', 'corollary',
% 'remark', and 'example' are defined in the LLNCS documentclass as well.
%
\begin{proof}
Proofs, examples, and remarks have the initial word in italics,
while the following text appears in normal font.
\end{proof}
For citations of references, we prefer the use of square brackets and consecutive numbers. Citations using labels or the author/year convention are also acceptable. The following bibliography provides a sample reference list with entries for journal
articles~\cite{Reviewer-Recommender}, an LNCS chapter~\cite{ref_lncs1}, a
book~\cite{ref_book1}, proceedings without editors~\cite{ref_proc1},
and a homepage~\cite{ref_url1}. Multiple citations are grouped
\cite{ref_article1,ref_lncs1,ref_book1},
\cite{ref_article1,ref_book1,ref_proc1,ref_url1}.
\subsubsection{Acknowledgements} Please place your acknowledgments at
the end of the paper, preceded by an unnumbered run-in heading (i.e.
3rd-level heading).
Table \ref{tab2} presents the results of experiments to determine the speed of the proposed algorithm.
\begin{table}[]
\caption{Results of experiments performed to evaluate the speed of the proposed algorithm.}\label{tab2}
\begin{tabular}{|llll|l|}
\hline
\multicolumn{1}{|l|}{} & \multicolumn{1}{l|}{Project name} & \multicolumn{1}{l|}{Parsing speed (min)} & Number of graph nodes & Number of rows processed per 1 minute \\ \hline
\multicolumn{1}{|l|}{1} & \multicolumn{1}{l|}{BaseRecycler} & \multicolumn{1}{l|}{1.564} & 844 & 2 491 \\ \hline
\multicolumn{1}{|l|}{2} & \multicolumn{1}{l|}{AlamazDev} & \multicolumn{1}{l|}{5.935} & 1 837 & 2 658 \\ \hline
\multicolumn{1}{|l|}{3} & \multicolumn{1}{l|}{SnakeBoom} & \multicolumn{1}{l|}{6.308} & 2 197 & 3 255 \\ \hline
\multicolumn{1}{|l|}{4} & \multicolumn{1}{l|}{retrofit} & \multicolumn{1}{l|}{11.817} & 7 118 & 2 718 \\ \hline
\multicolumn{1}{|l|}{5} & \multicolumn{1}{l|}{Glide} & \multicolumn{1}{l|}{14.556} & 8 496 & 2 576 \\ \hline
\multicolumn{1}{|l|}{6} & \multicolumn{1}{l|}{ZXing} & \multicolumn{1}{l|}{20.468} & 10 560 & 2 533 \\ \hline
\multicolumn{1}{|l|}{7} & \multicolumn{1}{l|}{RxJava} & \multicolumn{1}{l|}{22.777} & 11 972 & 2 814 \\ \hline
\multicolumn{1}{|l|}{8} & \multicolumn{1}{l|}{VisualProjectCore} & \multicolumn{1}{l|}{24.009} & 13 334 & 2 969 \\ \hline
\multicolumn{1}{|l|}{9} & \multicolumn{1}{l|}{mc-dev} & \multicolumn{1}{l|}{31.048} & 14 444 & 2 746 \\ \hline
\multicolumn{1}{|l|}{10} & \multicolumn{1}{l|}{xRayJavaTool} & \multicolumn{1}{l|}{35.613} & 23 946 & 2 730 \\ \hline
\multicolumn{4}{|l|}{Average value} & 2 750 \\ \hline
\end{tabular}
\end{table}
The experiment revealed that we processed an average of 2,750 lines of code per minute. Laboratory and coursework are on average 500-3000 lines of code. Thus, the processing speed of one laboratory on average will take less than one minute.
\section{Conclusion}
This article presents the results of developing an approach and a system for searching for structurally similar projects.
We completed the following tasks:
\begin{itemize}
\item we analyzed existing methods of source code analysis, including for determining originality of the project;
\item we developed an algorithm for constructing ASD in analyzing the source code of the project;
\item we developed an algorithm for determining originality of the project based on the analysis of the AST structure;
\item we implemented a software system to determine originality based on the analysis of its structure;
\item we conducted experiments to determine the speed of the proposed algorithm.
\end{itemize}
Thus, the developed system makes it possible to find borrowings in student projects in less than a minute on average.
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