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.
History
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 - Companion (ICSE-Companion)
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",
}