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.
|Title of host publication||Software Quality: Future Perspectives on Software Engineering Quality. SWQD 2021|
|Subtitle of host publication||Future Perspectives on Software Engineering Quality - 13th International Conference, SWQD 2021, Proceedings|
|Editors||Dietmar Winkler, Stefan Biffl, Daniel Mendez, Manuel Wimmer, Johannes Bergsmann|
|Number of pages||11|
|Publication status||Published - 2021|
|Name||Lecture Notes in Business Information Processing|
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- AI software
- Software quality