IMPORTANT DATES
- November 22, 2024 - abstract submission
- November 29, 2024 - paper submission
- December 20, 2024 - notification
- January 10, 2025 (strict) - camera-ready deadline
Artificial Intelligence (AI) is becoming increasingly important in our world, and is being included in a large number of applications and technologies that we use daily. Many AI-enabled applications are produced by developers without proper training in software quality practices or processes. An AI-enabled system is a software-based system that comprises non-trivial AI components in addition to traditional software components. As with any software system, AI-enabled systems require attention to software quality assurance (SQA) in general and code and design quality in particular. Agile development models enable companies to choose technologies to adopt in their systems at any development stage. Therefore, it is challenging to anticipate if a system, or a data pipeline used to develop AI, will produce high-quality models and high-quality results. The main reason for this challenge is that the AI engineering profession is very young, and there is limited training or guidelines on software quality issues (such as code quality, design quality, or testing) for AI-intensive applications. According to preliminary studies, developer training represents one of the biggest gaps in software quality assurance for AI, resulting in low-quality code and challenges with long-term maintenance. Moreover, the software quality of AI-enabled systems is often poorly tested.