ABOUT

Quality of AI-enabled systems (Q4AI) is recognized as a difficult challenge in both research and practice. Many of these challenges are driven by the data-dependent nature of AI components in which functionality is determined by characteristics (features) of training and operational data and not by traditional component specifications from which test cases are often derived. This data-dependency also causes AI components to drift over time as characteristics of operational data change over time, therefore requiring QA activities, such as runtime monitoring to be essential components of AI-enabled systems. 

A complementary aspect of Quality in the Age of AI is the use of AI to support Quality activities and processes (AI4Q), such as using AI techniques for test data and test case generation, fault localization in source code, and analyzing runtime log data to identify problems and courses of action. Challenges in this area stem from the lack of high quality and quantity of training data and oracles that are important for model performance and accuracy.

With the increase in complexity, size, and ubiquity of AI-enabled systems, as well as advances in AI including the growing popularity of large language models (LLMs), it is necessary to continue exploring Quality in the Age of AI. We therefore seek novel contributions investigating advances in both Q4AI and AI4Q.

TOPICS

The scope of this track is Quality in the Age of AI. The topics of interest include, but are not limited to:

• Quality of AI-enabled Systems:

  • Elicitation and specification of quality requirements for AI systems

  • Testing techniques for AI components and systems

  • Data quality processes

  • Tools to support software quality activities in AI systems

  • Runtime monitoring of AI systems

  • Certification processes for AI components and systems

  • Quality metrics for AI systems and components

• AI Supporting Software Quality Processes  

  • AI for test case generation

  • AI for test data generation

  • AI for quality requirements generation

  • AI for runtime log analysis

  • AI for fault localization

SUBMISSION

Formatting and submission guidelines are available at https://2024.quatic.org/call-for-papers . QUATIC uses a single submission site for all tracks. When submitting, please make sure to select “Quality in the age of AI” as your track.

TRACK COMMITTEE

Chairs:  Grace A. Lewis (Carnegie Mellon Software Engineering Institute, United States) and Domenico Bianculli (University of Luxembourg, Luxembourg)

Program Committee: 

• Alessio Gambi, AIT, Austria

• Donghwan Shin, University of Sheffield, United Kingdom

• Julien Siebert, Fraunhofer IESE, Germany

• Lei Ma, University of Tokyo, Japan and University of Alberta, Canada

• Neelofar Neelofar, Monash University, Australia 

• Rachel Brower-Sinning, Carnegie Mellon Software Engineering Institute, USA

• Vincenzo Riccio, University of Udine, Italy

• Xiaochen Li, Dalian University of Technology, China

• Xiao-Y Zhang, University of Science and Technology Beijing, China

• Zhenya Zhang, Kyushu University, Japan