Generating formal system models from natural language descriptions

By Rolf Drechsler, Ian G. Harris, Robert Wille. 2012

11 citations recorded by the source corpus.

Subfields: Information Systems, Software

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Siddharth Garg
2. Ramesh Karri
3. Hammond A. Pearce
4. Brendan Dolan-Gavitt
5. Benjamin Tan
6. Foutse Khomh
7. Baleegh Ahmad
8. Michel C. Desmarais

In Software

1. Brendan Dolan-Gavitt
2. Benjamin Tan
3. Foutse Khomh
4. Ramesh Karri
5. Baleegh Ahmad
6. Hammond A. Pearce

Citations in the corpus

• Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions (2021, neutral): “, [10], [11]) or via machine-learned NLP [12].”
• 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].”
• 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 [23] and formal models [10, 19] to Evolutionary Algorithms [33] and machine-learned translation [28].”
• 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.”
• DAVE: Deriving Automatically Verilog from English (2020, methodology): “such it is possible to extract this information and transform it into its corresponding computer code [9]. Other related works use NLP techniques,includingrule-basedprocessing,forformalsystemmodeling [4], generating hardware assertions [5], and for enhancing documentation by automatically extracting”
• Examining Zero-Shot Vulnerability Repair with Large Language Models (2021, methodology): “Given increasing complexity of software and the ongoing pursuit of development process efficiency, researchers have investigated myriad ways to support software development, such as tools for code completion [1], [3], [44]–[46] or recommending fixes based on compiler error messages by “crowdsourcing”