diff --git a/core/src/main/java/de/jplag/Messages.java b/core/src/main/java/de/jplag/Messages.java deleted file mode 100644 index b39549612..000000000 --- a/core/src/main/java/de/jplag/Messages.java +++ /dev/null @@ -1,21 +0,0 @@ -package de.jplag; - -import java.util.MissingResourceException; -import java.util.ResourceBundle; - -public class Messages { - private static final String BUNDLE_NAME = "de.jplag.messages"; //$NON-NLS-1$ - - private static final ResourceBundle RESOURCE_BUNDLE = ResourceBundle.getBundle(BUNDLE_NAME); - - private Messages() { - } - - public static String getString(String key) { - try { - return RESOURCE_BUNDLE.getString(key); - } catch (MissingResourceException e) { - return '!' + key + '!'; - } - } -} diff --git a/core/src/main/java/de/jplag/reporting/reportobject/model/Metric.java b/core/src/main/java/de/jplag/reporting/reportobject/model/Metric.java deleted file mode 100644 index d95b1f71a..000000000 --- a/core/src/main/java/de/jplag/reporting/reportobject/model/Metric.java +++ /dev/null @@ -1,9 +0,0 @@ -package de.jplag.reporting.reportobject.model; - -import java.util.List; - -import com.fasterxml.jackson.annotation.JsonProperty; - -public record Metric(@JsonProperty("name") String name, @JsonProperty("distribution") List distribution, - @JsonProperty("topComparisons") List topComparisons, @JsonProperty String description) { -} diff --git a/core/src/main/resources/de/jplag/messages.properties b/core/src/main/resources/de/jplag/messages.properties deleted file mode 100644 index bd4b78a25..000000000 --- a/core/src/main/resources/de/jplag/messages.properties +++ /dev/null @@ -1,29 +0,0 @@ -CommandLineArgument.BaseCode=Path of the directory containing the base code (common framework used in all submissions) -CommandLineArgument.Debug=Debug parser. Non-parsable files will be stored -CommandLineArgument.Suffixes=comma-separated list of all filename suffixes that are included -CommandLineArgument.ExcludeFile=All files named in this file will be ignored in the comparison (line-separated list) -CommandLineArgument.Language=Select the language to parse the submissions -CommandLineArgument.MinTokenMatch=Tunes the comparison sensitivity by adjusting the minimum token required to be counted as a matching section. A smaller increases the sensitivity but might lead to more false-positives -CommandLineArgument.ResultFolder=Name of the directory in which the comparison results will be stored -CommandLineArgument.RootDirectory=Root directories with submissions to check for plagiarism -CommandLineArgument.NewDirectory=Root directories with submissions to check for plagiarism (same as the root directory) -CommandLineArgument.OldDirectory=Root directories with prior submissions to compare against -CommandLineArgument.ShownComparisons=The maximum number of comparisons that will be shown in the generated report, if set to -1 all comparisons will be shown -CommandLineArgument.SimilarityThreshold=Comparison similarity threshold [0.0-1.0]: All comparisons above this threshold will be saved -CommandLineArgument.Subdirectory=Look in directories /*/ for programs -CommandLineArgument.ClusterDisable=Skips the clustering -CommandLineArgument.ClusterAlgorithm=Which clustering algorithm to use. Agglomerative merges similar submissions bottom up. Spectral clustering is combined with Bayesian Optimization to execute the k-Means clustering algorithm multiple times, hopefully finding a "good" clustering automatically. -CommandLineArgument.ClusterMetric=The metric used for clustering. AVG is intersection over union, MAX can expose some attempts of obfuscation. -CommandLineArgument.ClusterSpectralBandwidth=Bandwidth of the matern kernel in the Gaussian Process used during the search for a good number of clusters for spectral clustering. If a good clustering result is found during the search, numbers of clusters that differ by something in range of the bandwidth are also expected to good. -CommandLineArgument.ClusterSpectralNoise=The result of each run in the search for good clusterings are random. The noise level models the variance in the "worth" of these results. It also acts as a regularization constant. -CommandLineArgument.ClusterSpectralMinRuns=Minimum number of k-Means executions during spectral clustering. With these initial clustering sizes are explored. -CommandLineArgument.ClusterSpectralMaxRuns=Maximum number of k-Means executions during spectral clustering. Any execution after the initial runs tries to balance between exploration of unknown clustering sizes and exploitation of clustering sizes known as good. -CommandLineArgument.ClusterSpectralKmeansIterations=Maximum number of iterations during each execution of the k-Means algorithm. -CommandLineArgument.ClusterAgglomerativeThreshold=Only clusters with an inter-cluster-similarity greater than this threshold are merged during agglomerative clustering. -CommandLineArgument.ClusterAgglomerativeInterClusterSimilarity=How to measure the similarity of two clusters during agglomerative clustering. Minimum, maximum or average similarity between the submissions in each cluster. -CommandLineArgument.ClusterPreprocessingNone=Do not use any preprocessing before clustering. Not recommended for spectral clustering. -CommandLineArgument.ClusterPreprocessingCdf=Before clustering, the value of the cumulative distribution function of all similarities is estimated. The similarities are multiplied with these estimates. This has the effect of supressing similarities that are low compared to other similarities. -CommandLineArgument.ClusterPreprocessingPercentile=Any similarity smaller than the given percentile will be suppressed during clustering. -CommandLineArgument.ClusterPreprocessingThreshold=Any similarity smaller than the given threshold value will be suppressed during clustering. -SimilarityMetric.Avg.Description = Average of both program coverages. This is the default similarity which works in most cases: Matches with a high average similarity indicate that the programs work in a very similar way. -SimilarityMetric.Max.Description = Maximum of both program coverages. This ranking is especially useful if the programs are very different in size. This can happen when dead code was inserted to disguise the origin of the plagiarized program.