TY - GEN
T1 - Software Quality for AI
T2 - International Conference on Software Quality
AU - Lenarduzzi, Valentina
AU - Lomio, Francesco
AU - Moreschini, Sergio
AU - Taibi, Davide
AU - Tamburri, Damian Andrew
N1 - JUFOID=71106
PY - 2021
Y1 - 2021
N2 - Articial 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 Articial 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 Articial Intelligence systems, with a central focus on machine learning code, based on the experience of our four research groups. Moreover, we aim at dening 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 - Articial 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 Articial 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 Articial Intelligence systems, with a central focus on machine learning code, based on the experience of our four research groups. Moreover, we aim at dening 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 - Software Quality
KW - AI Software
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
A2 - Winkler, Dietmar
A2 - Biffl, Stefan
A2 - Mendez, Daniel
A2 - Wimmer, Manuel
A2 - Bergsmann, Johannes
PB - Springer
Y2 - 19 January 2021 through 21 January 2021
ER -