Software Testing for Machine Learning

04/30/2022
by   Dusica Marijan, et al.
0

Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question the widespread use of machine learning, especially in safety-critical applications, unless we are able to assure its correctness and trustworthiness properties. Software verification and testing are established technique for assuring such properties, for example by detecting errors. However, software testing challenges for machine learning are vast and profuse - yet critical to address. This summary talk discusses the current state-of-the-art of software testing for machine learning. More specifically, it discusses six key challenge areas for software testing of machine learning systems, examines current approaches to these challenges and highlights their limitations. The paper provides a research agenda with elaborated directions for making progress toward advancing the state-of-the-art on testing of machine learning.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/06/2021

A Review of Formal Methods applied to Machine Learning

We review state-of-the-art formal methods applied to the emerging field ...
research
06/13/2022

Specifying and Testing k-Safety Properties for Machine-Learning Models

Machine-learning models are becoming increasingly prevalent in our lives...
research
12/30/2022

Testing RESTful APIs: A Survey

In industry, RESTful APIs are widely used to build modern Cloud Applicat...
research
07/20/2023

Distributional Regression for Data Analysis

Flexible modeling of how an entire distribution changes with covariates ...
research
08/03/2021

Tutorials on Testing Neural Networks

Deep learning achieves remarkable performance on pattern recognition, bu...
research
10/01/2021

Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing

Testing has been widely recognised as difficult for AI applications. Thi...
research
05/03/2021

MLCheck- Property-Driven Testing of Machine Learning Models

In recent years, we observe an increasing amount of software with machin...

Please sign up or login with your details

Forgot password? Click here to reset