Program: 42564
Project: 42564
Buggy source:
public List<?> predict(int tenantId,String userName,long modelId,String[] data) throws MLModelHandlerException, MLModelBuilderException {
  if (!isValidModelId(tenantId,userName,modelId)) {
    String msg=String.format("String_Node_Str",modelId,tenantId,userName);
    throw new MLModelHandlerException(msg);
  }
  ClassLoader tccl=Thread.currentThread().getContextClassLoader();
  try {
    Thread.currentThread().setContextClassLoader(JavaSparkContext.class.getClassLoader());
    MLModelNew model=databaseService.getModel(tenantId,userName,modelId);
    String dataType=databaseService.getDataTypeOfModel(modelId);
    String columnSeparator=ColumnSeparatorFactory.getColumnSeparator(dataType);
    SparkConf sparkConf=MLCoreServiceValueHolder.getInstance().getSparkConf();
    Workflow facts=databaseService.getWorkflow(modelId,model.getAnalysisId());
    MLModelConfigurationContext context=new MLModelConfigurationContext();
    context.setModelId(modelId);
    context.setColumnSeparator(columnSeparator);
    context.setFacts(facts);
    context.setDataToBePredicted(data);
    JavaSparkContext sparkContext=null;
    sparkConf.setAppName(String.valueOf(modelId));
    sparkContext=new JavaSparkContext(sparkConf);
    context.setSparkContext(sparkContext);
    MLModel builtModel=retrieveModel(modelId);
    Predictor predictor=new Predictor(modelId,builtModel,context);
    List<?> predictions=predictor.predict();
    log.info(String.format("String_Node_Str",modelId));
    return predictions;
  }
 catch (  DatabaseHandlerException e) {
    throw new MLModelBuilderException("String_Node_Str" + e.getMessage(),e);
  }
 finally {
    Thread.currentThread().setContextClassLoader(tccl);
  }
}

Fixed version (ground truth):
public List<?> predict(int tenantId,String userName,long modelId,String[] data) throws MLModelHandlerException, MLModelBuilderException {
  if (!isValidModelId(tenantId,userName,modelId)) {
    String msg=String.format("String_Node_Str",modelId,tenantId,userName);
    throw new MLModelHandlerException(msg);
  }
  ClassLoader tccl=Thread.currentThread().getContextClassLoader();
  try {
    Thread.currentThread().setContextClassLoader(JavaSparkContext.class.getClassLoader());
    MLModelNew model=databaseService.getModel(tenantId,userName,modelId);
    String dataType=databaseService.getDataTypeOfModel(modelId);
    String columnSeparator=ColumnSeparatorFactory.getColumnSeparator(dataType);
    SparkConf sparkConf=MLCoreServiceValueHolder.getInstance().getSparkConf();
    Workflow facts=databaseService.getWorkflow(model.getAnalysisId());
    MLModelConfigurationContext context=new MLModelConfigurationContext();
    context.setModelId(modelId);
    context.setColumnSeparator(columnSeparator);
    context.setFacts(facts);
    context.setDataToBePredicted(data);
    JavaSparkContext sparkContext=null;
    sparkConf.setAppName(String.valueOf(modelId));
    sparkContext=new JavaSparkContext(sparkConf);
    context.setSparkContext(sparkContext);
    MLModel builtModel=retrieveModel(modelId);
    Predictor predictor=new Predictor(modelId,builtModel,context);
    List<?> predictions=predictor.predict();
    log.info(String.format("String_Node_Str",modelId));
    return predictions;
  }
 catch (  DatabaseHandlerException e) {
    throw new MLModelBuilderException("String_Node_Str" + e.getMessage(),e);
  }
 finally {
    Thread.currentThread().setContextClassLoader(tccl);
  }
}