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

arXiv: https://arxiv.org/abs/2012.11163 · HTML: https://mlsanity.com/paper/2012.11163/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation](https://mlsanity.com/paper/2212.09994/) (2022)
- [Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing](https://mlsanity.com/paper/2112.01956/) (2021)
- [BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning](https://mlsanity.com/paper/2606.02109/) (2026)

## Abstract (arXiv)

Text-to-SQL is a task to generate SQL queries from human utterances. However, due to the variation of natural language, two semantically equivalent utterances may appear differently in the lexical level. Likewise, user preferences (e.g., the choice of normal forms) can lead to dramatic changes in table structures when expressing conceptually identical schemas. Envisioning the general difficulty for text-to-SQL models to preserve prediction consistency against linguistic and schema variations, we propose MT-Teql, a Metamorphic Testing-based framework for systematically evaluating and augmenting the consistency of TExt-to-SQL models. Inspired by the principles of software metamorphic testing, MT-Teql delivers a model-agnostic framework which implements a comprehensive set of metamorphic relations (MRs) to conduct semantics-preserving transformations toward utterances and schemas. Model Inconsistency can be exposed when the original and transformed inputs induce different SQL queries. In addition, we leverage the transformed inputs to retrain models for further model robustness boost. Our experiments show that our framework exposes thousands of prediction errors from SOTA models and enriches existing datasets by order of magnitude, eliminating over 40% inconsistency errors without compromising standard accuracy.