How to Use

[SimDing]

Clustering

By default, JPlag is configured to perform a clustering of the submissions.
The clustering partitions the set of submissions into groups of similar submissions.
The found clusters can be used candidates for potentially colluding groups. Each cluster has a strength score, that measures how suspicious the cluster is compared to other clusters.

Disabling Clustering

Clustering can take long when there is a large amount of submissions.
Users who are not interested in the clustering can safely disable it:

• Using the CLI: With the --cluster-skip option
• Programmatically:

  JPlagOptions options = new JPlagOptions("/path/to/rootDir", LanguageOption.JAVA);
  options.setClusteringOptions(new ClusteringOptions.Builder().enabled(false).build());
  
  JPlag jplag = new JPlag(options);

Clustering Configuration

Clustering can either be configured using the CLI options or programmatically using the ClusteringOptions class. Both options work analogous and share the same default values.

The clustering it designed to work out-of-the-box for running within the magnitude of about 50-500 submissions, but it can be tweaked when problems occur. For more submissions it might be necessary to increase Max-Runs or Bandwidth, so that an appropriate number of clusters can be determined.

Group	Option	Description	Default
General	Enable	Controls whether the clustering is run at all.	true
General	Algorithm	Which clustering algorithm to use. Agglomerative Clustering Agglomerative Clustering iteratively merges similar submissions bottom up. It usually requires manual tuning for it's parameters to yield helpful clusters. Spectral Clustering Spectral Clustering is combined with Bayesian Optimization to execute the k-Means clustering algorithm multiple times, hopefully finding a "good" clustering automatically. It's default parameters should work O.K. in most cases.	Spectral Clustering
General	Metric	The similarity score between submissions to use during clustering. Each score is expressed in terms of the size of the submissions A and B and the size of their matched intersection A ∩ B. AVG (aka. Dice's coefficient) AVG = 2 * (A ∩ B) / (A + B) MAX (aka. overlap coefficient) MAX = (A ∩ B) / min(A, B) Compared to MAX, this prevents obfuscation when a collaborator bloats his submission with unrelated code. MIN (deprecated) MIN = (A ∩ B) / max(A, B) INTERSECTION (experimental) INTERSECTION = A ∩ B	MAX
Spectral	Bandwidth	For Spectral Clustering, Baysian Optimization is used to determine a fitting number of clusters. 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. Low values result in more exploration of the search space, high values in more exploitation of known results.	20.0
Spectral	Noise	The result of each k-Means run in the search for good clusterings is random. The noise level models the variance in the "worth" of these results. It also acts as a regularization constant.	0.0025
Spectral	Min-Runs	Minimum number of k-Means executions for spectral clustering. With these initial runs clustering sizes are explored.	5
Spectral	Max-Runs	Maximum number of k-Means executions during spectral clustering. Any execution after the initial (min-) runs tries to balance between exploration of unknown clustering sizes and exploitation of clustering sizes known as good.	50
Spectral	K-Means Iterations	Maximum number of iterations during each execution of the k-Means algorithm.	200
Agglomerative	Threshold	Only clusters with an inter-cluster-similarity greater than this threshold are merged during agglomerative clustering.	0.2
Agglomerative	inter-cluster-similarity	How to measure the similarity of two clusters during agglomerative clustering. MIN (aka. complete-linkage) Clusters are merged if all their submissions are similar. MAX (aka. single-linkage) Clusters are merged if there is a similar submission in both. AVERAGE (aka. average-linkage) Clusters are merged if their submissions are similar on average.	AVERAGE
Preprocessing	Pre-Processor	How the similarities are preprocessed prior to clustering. Spectral Clustering will probably not have good results without it. None No preprocessing. Cumulative Distribution Function (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 suppressing similarities that are low compared to other similarities. Percentile Any similarity smaller than the given percentile will be suppressed during clustering. Threshold Any similarity smaller than the given threshold will be suppressed during clustering.	CDF