  The massive progress of machine learning has seen its application over a
variety of domains in the past decade. But how do we develop a systematic,
scalable and modular strategy to validate machine-learning systems? We present,
to the best of our knowledge, the first approach, which provides a systematic
test framework for machine-learning systems that accepts grammar-based inputs.
Our OGMA approach automatically discovers erroneous behaviours in classifiers
and leverages these erroneous behaviours to improve the respective models. OGMA
leverages inherent robustness properties present in any well trained
machine-learning model to direct test generation and thus, implementing a
scalable test generation methodology. To evaluate our OGMA approach, we have
tested it on three real world natural language processing (NLP) classifiers. We
have found thousands of erroneous behaviours in these systems. We also compare
OGMA with a random test generation approach and observe that OGMA is more
effective than such random test generation by up to 489%.
