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Text-to-SQL is a task to generate SQL queries from human utterances. However,due to the variation of natural language, two semantically equivalentutterances may appear differently in the lexical level. Likewise, userpreferences (e.g., the choice of normal forms) can lead to dramatic changes intable structures when expressing conceptually identical schemas. Envisioningthe general difficulty for text-to-SQL models to preserve predictionconsistency against linguistic and schema variations, we propose MT-Teql, aMetamorphic Testing-based framework for systematically evaluating andaugmenting the consistency of TExt-to-SQL models. Inspired by the principles ofsoftware metamorphic testing, MT-Teql delivers a model-agnostic framework whichimplements a comprehensive set of metamorphic relations (MRs) to conductsemantics-preserving transformations toward utterances and schemas. ModelInconsistency can be exposed when the original and transformed inputs inducedifferent SQL queries. In addition, we leverage the transformed inputs toretrain models for further model robustness boost. Our experiments show thatour framework exposes thousands of prediction errors from SOTA models andenriches existing datasets by order of magnitude, eliminating over 40%inconsistency errors without compromising standard accuracy.

MT-Teql: Evaluating and Augmenting Consistency of Text-to-SQL Models with Metamorphic Testing