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Learning Program Models from Generated Inputs

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conference contribution
posted on 2023-11-29, 18:25 authored by Tural MammadovTural Mammadov
Recent advances in Machine Learning (ML) show that Neural Machine Translation (NMT) models can mock the program behavior when trained on input-output pairs. Such models can mock the functionality of existing programs and serve as quick-to-deploy reverse engineering tools. Still, the problem of automatically learning such predictive and reversible models from programs needs to be solved. This work introduces a generic approach for automated and reversible program behavior modeling. It achieves 94% of overall accuracy in the conversion of Markdown-to-HTML and HTML-to-Markdown markups.

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Preferred Citation

Tural Mammadov. Learning Program Models from Generated Inputs. In: International Conference on Software Engineering (ICSE). 2023.

Primary Research Area

  • Secure Connected and Mobile Systems

Name of Conference

International Conference on Software Engineering (ICSE)

Legacy Posted Date

2023-03-08

Open Access Type

  • Unknown

BibTeX

@inproceedings{cispa_all_3903, title = "Learning Program Models from Generated Inputs", author = "Mammadov, Tural", booktitle="{International Conference on Software Engineering (ICSE)}", year="2023", }

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