mlsanityData to 7 Oct 2026

Towards Property-Based Tests in Natural Language

arXiv 2202.03616 ↗First posted 8 Feb 2022cs.SE cs.CLcited by 1

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Abstract (arXiv)

We consider a new approach to generate tests from natural language. Rather than relying on machine learning or templated extraction from structured comments, we propose to apply classic ideas from linguistics to translate natural-language sentences into executable tests. This paper explores the application of combinatory categorial grammars (CCGs) to generating property-based tests. Our prototype is able to generate tests from English descriptions for each example in a textbook chapter on property-based testing.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 66 references that are in the map. First posted on arXiv 8 Feb 2022.

Builds on

7 of 66 references.

References · 7

  1. 2019What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language modelscited by 317
  2. 2017A Survey of Machine Learning for Big Code and Naturalnesscited by 220
  3. 2020Improved Code Summarization via a Graph Neural Networkcited by 79
  4. 2019A Neural Model for Generating Natural Language Summaries of Program Subroutinescited by 76
  5. 2017The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representationscited by 41
  6. 2017A* CCG Parsing with a Supertag and Dependency Factored Modelcited by 22
  7. 2021A CCG-Based Version of the DisCoCat Frameworkcited by 16

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Trustworthy Formal Natural Language Specificationscited by 0