- Paper Submission Deadline: December 17th, 2021
- Notification: January 7th, 2022
- Camera ready: January 12th, 2022
- Workshop: March 18th, 2022
Artificial Intelligence (AI) is getting more and more popular, being adopted in a large number of applications and technologies we use on a daily basis. A large number of AI-enabled applications are produced by developers without proper training on software quality practices or processes, and in general, lack state-of-the-art software engineering processes. An AI-enabled system is a software-based system that comprises AI components besides traditional software components. As any software system, AI-enabled systems require attention to software quality assurance (SQA) in general and code quality in particular. Current development processes, and in particular agile development models, enable companies to decide on the technologies to adopt in their system in a later stage. Therefore, it is hard to anticipate if a system, or if a data pipeline used to develop AI will produce high-quality models. The main reason is due to the fact that the AI engineer profession was born very recently, and currently there is a very limited number of training or guidelines on issues (such as code quality or testing) for AI and applications using AI code. According to preliminary studies, developers' training is one of the biggest lacks in software quality assurance for AI, which usually brings several issues related to low quality of AI code as well as low long-term maintenance. Moreover, the software quality of AI-enabled system is often poorly tested and of very low quality.