Learning Program Models from Generated Inputs

Learning Program Models from Generated Inputs

연구 분야: Analysis

학회: ICSE '23: Proceedings of the 45th International Conference on Software Engineering: Companion Proceedings

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.