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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 <sc>Ogma</sc> approach automatically discovers erroneous behaviours in classifiers and leverages these erroneous behaviours to improve the respective models. <sc>Ogma</sc> 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 <sc>Ogma</sc> 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 <sc>Ogma</sc> with a random test generation approach and observe that <sc>Ogma</sc> is more effective than such random test generation by up to 489 percent.

Grammar Based Directed Testing of Machine Learning Systems