On the Testability of Artificial Intelligence and Machine Learning Systems

Raghvinder S. Sangwan; Youakim Badr; Satish Mahade Van Srinivasan; Partha Mukherjee; Phil Laplante

doi:10.1109/MC.2021.3132710

On the Testability of Artificial Intelligence and Machine Learning Systems

Raghvinder S. Sangwan
, Youakim Badr
, Satish Mahade Van Srinivasan
, Partha Mukherjee
, Phil Laplante

Research output: Contribution to specialist publication › Article

4 Scopus citations

Abstract

This article examines current testing techniques for the quality assurance of artificial intelligence and machine learning systems. It organizes them based on the granularity of testing level and explores design tactics using these techniques.

Original language	English (US)
Pages	101-105
Number of pages	5
Volume	55
No	3
Specialist publication	Computer
DOIs	https://doi.org/10.1109/MC.2021.3132710
State	Published - Mar 1 2022

All Science Journal Classification (ASJC) codes

General Computer Science

Access to Document

10.1109/MC.2021.3132710

Cite this

@misc{8d0ad50d4d9248f39fe2b566f69829d0,

title = "On the Testability of Artificial Intelligence and Machine Learning Systems",

abstract = "This article examines current testing techniques for the quality assurance of artificial intelligence and machine learning systems. It organizes them based on the granularity of testing level and explores design tactics using these techniques.",

author = "Sangwan, \{Raghvinder S.\} and Youakim Badr and Srinivasan, \{Satish Mahade Van\} and Partha Mukherjee and Phil Laplante",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.",

year = "2022",

month = mar,

day = "1",

doi = "10.1109/MC.2021.3132710",

language = "English (US)",

volume = "55",

pages = "101--105",

journal = "Computer",

issn = "0018-9162",

publisher = "IEEE Computer Society",

}

TY - GEN

T1 - On the Testability of Artificial Intelligence and Machine Learning Systems

AU - Sangwan, Raghvinder S.

AU - Badr, Youakim

AU - Srinivasan, Satish Mahade Van

AU - Mukherjee, Partha

AU - Laplante, Phil

PY - 2022/3/1

Y1 - 2022/3/1

N2 - This article examines current testing techniques for the quality assurance of artificial intelligence and machine learning systems. It organizes them based on the granularity of testing level and explores design tactics using these techniques.

AB - This article examines current testing techniques for the quality assurance of artificial intelligence and machine learning systems. It organizes them based on the granularity of testing level and explores design tactics using these techniques.

UR - https://www.scopus.com/pages/publications/85127590959

UR - https://www.scopus.com/pages/publications/85127590959#tab=citedBy

U2 - 10.1109/MC.2021.3132710

DO - 10.1109/MC.2021.3132710

M3 - Article

AN - SCOPUS:85127590959

SN - 0018-9162

VL - 55

SP - 101

EP - 105

JO - Computer

JF - Computer

ER -

On the Testability of Artificial Intelligence and Machine Learning Systems

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this