作者
Vincenzo Riccio, Gunel Jahangirova, Andrea Stocco, Nargiz Humbatova, Michael Weiss, Paolo Tonella
发表日期
2020/11
来源
Empirical Software Engineering
卷号
25
页码范围
5193-5254
出版商
Springer US
简介
Context
A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The increasing adoption of MLSs in safety critical domains such as autonomous driving, healthcare, and finance has fostered much attention towards the quality assurance of such systems. Despite the advances in software testing, MLSs bring novel and unprecedented challenges, since their behaviour is defined jointly by the code that implements them and the data used for training them.
Objective
To identify the existing solutions for functional testing of MLSs, and classify them from three different perspectives: (1) the context of the problem they address, (2) their features, and (3) their empirical evaluation. To report demographic information about the ongoing research. To identify open challenges for future research.
Method
We conducted a systematic …
引用总数
20202021202220232024537526041
学术搜索中的文章
V Riccio, G Jahangirova, A Stocco, N Humbatova… - Empirical Software Engineering, 2020