Strategy to create a testbed and gold standard I need to create a correct evaluation of the method I'm proposing. The problem is that in the field I'm working on there is no benchmark that I can use for this purpose. On the other hand, my method has to run, as much as possible, on top of "real documents", but these documents are mostly private and created by industry. 

The documents are based on the AutomationML language, which is XML-based, and I do have some "real" ones. Until now, I have created the documents manually, and have also automatically generated them based on a given seed document and the AutomationML XML-Schema to ensure that the generated documents are syntactically correct. Also, they are generated following a uniform distribution. 

I would like to extend the automatic generation but for this, I don't know how to automatically create the gold standard. I would appreciate any hints regarding this.
You can't.  You can't squeeze blood out of a stone.  If you want to evaluate how well your method will work on real documents, you need real documents.  With synthetically/artificially generated documents, you'll never know if they have the same characteristics as real documents.

You don't necessarily need a standard benchmark that others have used -- but you do need to assemble a representative set of real documents to evaluate on.  Basically, if there is no standard benchmark in your field, you'll have to assemble one yourself.

Yes, this is difficult to do.  This is one of the less-well-known and less-glamorous aspects of working on machine learning: in practical projects, often we spend 80% of our time (or more!) just assembling data sets, and only 20% on the actual learning algorithms themselves.