Learning Program Models from Generated Inputs

(2023) Learning Program Models from Generated Inputs.

Text ICSE2023_Doctoral_Symposium_Tural_Mammadov.pdf Download (405kB)

Abstract

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.

Item Type:	Conference or Workshop Item (A Paper) (Paper)
Uncontrolled Keywords:	software testing, security testing, reverse engi- neering, deep learning
Divisions:	Andreas Zeller (Software Engineering, ST)
Conference:	ICSE International Conference on Software Engineering
Depositing User:	Tural Mammadov
Date Deposited:	08 Mar 2023 12:21
Last Modified:	28 Aug 2023 06:27
Primary Research Area:	NRA4: Secure Mobile and Autonomous Systems
URI:	https://publications.cispa.saarland/id/eprint/3903

Actions

Actions (login required)

View Item