Learning Program Models from Generated Inputs

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 autom...

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Bibliographic Details
Published in:Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) pp. 245 - 247
Main Author: Mammadov, Tural
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2023
Subjects:
Behavioral sciences
deep learning
Machine learning
Machine translation
Predictive models
Reverse engineering
security testing
Software
Software engineering
software testing
ISSN:2574-1934
Online Access:Get full text
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Description
Summary: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.
ISSN:2574-1934
DOI:10.1109/ICSE-Companion58688.2023.00066