Data Synthesis for Testing Black-Box Machine Learning Models

Autor:	Saha, Diptikalyan, Aggarwal, Aniya, Hans, Sandeep
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	The increasing usage of machine learning models raises the question of the reliability of these models. The current practice of testing with limited data is often insufficient. In this paper, we provide a framework for automated test data synthesis to test black-box ML/DL models. We address an important challenge of generating realistic user-controllable data with model agnostic coverage criteria to test a varied set of properties, essentially to increase trust in machine learning models. We experimentally demonstrate the effectiveness of our technique. Comment: Accepted as a 4-pages short paper in Research track at CODS-COMAD 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2111.02161 Zobrazit plný text záznamu View this record from Arxiv