Benutzung: jplag [-h] [-l {java,python3,cpp,csharp,char,text,scheme}] [-bc BC] [-v {quiet,long}] [-d] [-S S] [-p P] [-x X] [-t T] [-m M] [-n N] [-r R] [-c {normal,parallel}]
                 [--cluster-skip] [--cluster-alg {AGGLOMERATIVE,SPECTRAL}] [--cluster-metric {AVG,MIN,MAX,INTERSECTION}] [--cluster-spectral-bandwidth bandwidth]
                 [--cluster-spectral-noise noise] [--cluster-spectral-min-runs min] [--cluster-spectral-max-runs max] [--cluster-spectral-kmeans-interations iterations]
                 [--cluster-agglomerative-threshold threshold] [--cluster-agglomerative-inter-cluster-similarity {MIN,MAX,AVERAGE}] [--cluster-pp-none | --cluster-pp-cdf |
                 --cluster-pp-percentile percentile | --cluster-pp-threshold threshold] rootDir [rootDir ...]

JPlag - Maintained by SDQ
Created by IPD Tichy, Guido Malpohl, and others. JPlag logo designed by Sandro Koch. Currently maintained by Sebastian Hahner and Timur Saglam.

Positions-Argumente:
  rootDir                Root-directory that contains submissions

Benannte Argumente:
  -h, --help             zeigt diese Hilfe und beendet sich.
  -l {java,python3,cpp,csharp,char,text,scheme}
                         Select the language to parse the submissions (Standard: java)
  -bc BC                 Path of the directory containing the base code (common framework used in all submissions)
  -v {quiet,long}        Verbosity of the logging (Standard: quiet)
  -d                     Debug parser. Non-parsable files will be stored (Standard: false)
  -S S                   Look in directories <root-dir>/*/<dir> for programs
  -p P                   comma-separated list of all filename suffixes that are included
  -x X                   All files named in this file will be ignored in the comparison (line-separated list)
  -t T                   Tunes the comparison sensitivity by adjusting  the  minimum  token  required  to  be  counted  as  a  matching  section. A smaller <n> increases the
                         sensitivity but might lead to more false-positives
  -m M                   Comparison similarity threshold [0-100]: All comparisons above this threshold will be saved (Standard: 0.0)
  -n N                   The maximum number of comparisons that will be shown in the generated report, if set to -1 all comparisons will be shown (Standard: 30)
  -r R                   Name of the directory in which the comparison results will be stored (Standard: result)
  -c {normal,parallel}   Comparison mode used to compare the programs (Standard: normal)

Clustering:
  --cluster-skip         Skips the clustering (Standard: false)
  --cluster-alg {AGGLOMERATIVE,SPECTRAL}
                         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. (Standard: SPECTRAL)
  --cluster-metric {AVG,MIN,MAX,INTERSECTION}
                         The metric used for clustering. AVG is intersection over union, MAX can expose some attempts of obfuscation. (Standard: MAX)
  --cluster-spectral-bandwidth bandwidth
                         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.
                         (Standard: 20.0)
  --cluster-spectral-noise noise
                         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. (Standard: 0.0025000002)
  --cluster-spectral-min-runs min
                         Minimum number of k-Means executions during spectral clustering. With these initial clustering sizes are explored. (Standard: 5)
  --cluster-spectral-max-runs max
                         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. (Standard: 50)
  --cluster-spectral-kmeans-interations iterations
                         Maximum number of iterations during each execution of the k-Means algorithm. (Standard: 200)
  --cluster-agglomerative-threshold threshold
                         Only clusters with an inter-cluster-similarity greater than this threshold are merged during agglomerative clustering. (Standard: 0.2)
  --cluster-agglomerative-inter-cluster-similarity {MIN,MAX,AVERAGE}
                         How to measure the similarity of two clusters during  agglomerative  clustering.  Minimum,  maximum or average similarity between the submissions in
                         each cluster. (Standard: AVERAGE)

Clustering - Preprocessing:
  --cluster-pp-none      Do not use any preprocessing before clustering. Not recommended for spectral clustering. (Standard: false)
  --cluster-pp-cdf       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. (Standard: false)
  --cluster-pp-percentile percentile
                         Any similarity smaller than the given percentile will be suppressed during clustering.
  --cluster-pp-threshold threshold
                         Any similarity smaller than the given threshold value will be suppressed during clustering.