Program: 143966
Project: 143966
Buggy source:
public static void main(String[] args){
  int numberOfExperimentRepetitions=2;
  int numberOfExamplesToPickPerIteration=1000000;
  int maximumNumberOfIterations=1000000000;
  boolean includeTitleInResultsPlot=true;
  boolean includeHorizontalAxisLabelInResultsPlot=true;
  boolean includeVerticalAxisLabelInResultsPlot=true;
  boolean includeLegendInResultsPlot=true;
  ScoringFunction[] scoringFunctions=new ScoringFunction[]{new EntropyScoringFunction(),new EntropyScoringFunction(true),new ConstraintPropagationScoringFunction(true),new ConstraintPropagationScoringFunction(SurpriseFunction.NEGATIVE_LOGARITHM,true),new ConstraintPropagationScoringFunction(SurpriseFunction.ONE_MINUS_PROBABILITY,true)};
  Set<ResultType> resultTypes=new LinkedHashSet<>();
  resultTypes.add(ResultType.AUC_1_ITERATIONS_FULL_DATA_SET);
  resultTypes.add(ResultType.AVERAGE_AUC_FULL_DATA_SET);
  resultTypes.add(ResultType.NUMBER_OF_EXAMPLES_PICKED);
  boolean useLogicIntegrator=args[0].equals("String_Node_Str");
  boolean retrainClassifiers=args[1].equals("String_Node_Str");
  double negativeToPositiveTrainingExamplesRatio=1;
  ExamplePickingMethod examplePickingMethod=ExamplePickingMethod.PSEUDO_SEQUENTIAL;
  String workingDirectory;
  ImportedDataSet dataSet;
  Map<ScoringFunction,List<ExperimentResults>> results;
  logger.info("String_Node_Str");
  numberOfExperimentRepetitions=10;
  numberOfExamplesToPickPerIteration=1000;
  maximumNumberOfIterations=100000;
  workingDirectory="String_Node_Str";
  dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
  String resultsDirectoryName;
  if (retrainClassifiers && useLogicIntegrator)   resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else   if (retrainClassifiers)   resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else   if (useLogicIntegrator)   resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else   resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
  dataSet.exportStatistics(workingDirectory + "String_Node_Str");
  results=runExperiments(numberOfExperimentRepetitions,numberOfExamplesToPickPerIteration,maximumNumberOfIterations,scoringFunctions,examplePickingMethod,useLogicIntegrator,retrainClassifiers,dataSet,resultTypes);
  ConstrainedLearningExperiment.exportResults(numberOfExperimentRepetitions,numberOfExamplesToPickPerIteration,maximumNumberOfIterations,results,workingDirectory + resultsDirectoryName,resultTypes,includeTitleInResultsPlot,includeHorizontalAxisLabelInResultsPlot,includeVerticalAxisLabelInResultsPlot,includeLegendInResultsPlot);
  logger.info("String_Node_Str");
}

Fixed version (ground truth):
public static void main(String[] args){
  int numberOfExperimentRepetitions=2;
  int numberOfExamplesToPickPerIteration=1000000;
  int maximumNumberOfIterations=1000000000;
  boolean includeTitleInResultsPlot=true;
  boolean includeHorizontalAxisLabelInResultsPlot=true;
  boolean includeVerticalAxisLabelInResultsPlot=true;
  boolean includeLegendInResultsPlot=true;
  ScoringFunction[] scoringFunctions=new ScoringFunction[]{new EntropyScoringFunction(),new EntropyScoringFunction(true),new ConstraintPropagationScoringFunction(true),new ConstraintPropagationScoringFunction(SurpriseFunction.NEGATIVE_LOGARITHM,true),new ConstraintPropagationScoringFunction(SurpriseFunction.ONE_MINUS_PROBABILITY,true)};
  Set<ResultType> resultTypes=new LinkedHashSet<>();
  resultTypes.add(ResultType.AUC_1_ITERATIONS_FULL_DATA_SET);
  resultTypes.add(ResultType.AVERAGE_AUC_FULL_DATA_SET);
  resultTypes.add(ResultType.NUMBER_OF_EXAMPLES_PICKED);
  boolean useLogicIntegrator=args[0].equals("String_Node_Str");
  boolean retrainClassifiers=args[1].equals("String_Node_Str");
  double negativeToPositiveTrainingExamplesRatio=1;
  ExamplePickingMethod examplePickingMethod=ExamplePickingMethod.PSEUDO_SEQUENTIAL;
  String workingDirectory;
  ImportedDataSet dataSet;
  Map<ScoringFunction,List<ExperimentResults>> results;
  logger.info("String_Node_Str");
  numberOfExperimentRepetitions=10;
  numberOfExamplesToPickPerIteration=1;
  maximumNumberOfIterations=1000;
  workingDirectory="String_Node_Str";
  dataSet=importISOLETDataSet(workingDirectory,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
  String resultsDirectoryName;
  if (retrainClassifiers && useLogicIntegrator)   resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else   if (retrainClassifiers)   resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else   if (useLogicIntegrator)   resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else   resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
  dataSet.exportStatistics(workingDirectory + "String_Node_Str");
  results=runExperiments(numberOfExperimentRepetitions,numberOfExamplesToPickPerIteration,maximumNumberOfIterations,scoringFunctions,examplePickingMethod,useLogicIntegrator,retrainClassifiers,dataSet,resultTypes);
  ConstrainedLearningExperiment.exportResults(numberOfExperimentRepetitions,numberOfExamplesToPickPerIteration,maximumNumberOfIterations,results,workingDirectory + resultsDirectoryName,resultTypes,includeTitleInResultsPlot,includeHorizontalAxisLabelInResultsPlot,includeVerticalAxisLabelInResultsPlot,includeLegendInResultsPlot);
  logger.info("String_Node_Str");
}