TY - GEN
T1 - Software Quality for AI - Where We Are Now?
AU - Lenarduzzi, Valentina
AU - Lomio, Francesco
AU - Moreschini, Sergio
AU - Taibi, Davide
AU - Tamburri, Damian Andrew
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021
Y1 - 2021
N2 - Artificial Intelligence is getting more and more popular, being adopted in a large number of applications and technology we use on a daily basis. However, a large number of Artificial Intelligence applications are produced by developers without proper training on software quality practices or processes, and in general, lack in-depth knowledge regarding software engineering processes. The main reason is due to the fact that the machine-learning engineer profession has been born very recently, and currently there is a very limited number of training or guidelines on issues (such as code quality or testing) for machine learning and applications using machine learning code. In this work, we aim at highlighting the main software quality issues of Artificial Intelligence systems, with a central focus on machine learning code, based on the experience of our four research groups. Moreover, we aim at defining a shared research road map, that we would like to discuss and to follow in collaboration with the workshop participants. As a result, the software quality of AI-enabled systems is often poorly tested and of very low quality.
AB - Artificial Intelligence is getting more and more popular, being adopted in a large number of applications and technology we use on a daily basis. However, a large number of Artificial Intelligence applications are produced by developers without proper training on software quality practices or processes, and in general, lack in-depth knowledge regarding software engineering processes. The main reason is due to the fact that the machine-learning engineer profession has been born very recently, and currently there is a very limited number of training or guidelines on issues (such as code quality or testing) for machine learning and applications using machine learning code. In this work, we aim at highlighting the main software quality issues of Artificial Intelligence systems, with a central focus on machine learning code, based on the experience of our four research groups. Moreover, we aim at defining a shared research road map, that we would like to discuss and to follow in collaboration with the workshop participants. As a result, the software quality of AI-enabled systems is often poorly tested and of very low quality.
KW - AI software
KW - Software quality
UR - http://www.scopus.com/inward/record.url?scp=85101567404&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-65854-0_4
DO - 10.1007/978-3-030-65854-0_4
M3 - Conference contribution
SN - 978-3-030-65853-3
T3 - Lecture Notes in Business Information Processing
SP - 43
EP - 53
BT - Software Quality: Future Perspectives on Software Engineering Quality. SWQD 2021
A2 - Winkler, Dietmar
A2 - Biffl, Stefan
A2 - Mendez, Daniel
A2 - Wimmer, Manuel
A2 - Bergsmann, Johannes
PB - Springer
ER -