GLAsT: Learning formal grammars to translate natural language specifications into hardware assertions
By Christopher B. Harris, Ian G. Harris. 2016
52 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Software
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In Computational Theory and Mathematics
In Software
Citations in the corpus
- Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions (2021, neutral): “There are many efforts to automatically translate specifications into computer code for natural language programming [9], through formal models for automatic code generation (e.g., [10], [11]) or via machine-learned NLP [12].”
- VeriGen: A Large Language Model for Verilog Code Generation (2023, background): “Prior work explored NLP techniques for formal system modeling [9] and generating assertions [12], albeit on a small scale.”
- GitHub Copilot AI pair programmer: Asset or Liability? (2022, background): “One finds different approaches to automatic code generation in the literature, from natural language programming [35] and formal models [15, 27] to Evolutionary Algorithms [48] and machine-learned translation [42].”
- An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions (2021, background): “There are many efforts to automatically translate specifications into computer code for natural language programming [9], through formal models for automatic code generation (e.g., [10], [11]) or via machine-learned NLP [12].”
- DAVE: Deriving Automatically Verilog from English (2020, methodology): “Other related works use NLP techniques, including rule-based processing, for formal systemmodeling [4], generating hardware assertions [5], and for enhancing documentation by automatically extracting software development tasks and associating themwith the relevant paragraphs [16].”