by Hyo Jin Do (IBM Research, US), Molly Q. Feldman (Oberlin College, US), Jessica He (IBM Research, US), Angel Hsing-Chi Hwang (University…

IBM’s AI attribution toolkit helps users describe how AI contributed to their work.

by Osnat Mokryn (University of Haifa, IL), Orit Shaer (Wellesley College, US), Werner Geyer (IBM Research, US), Mary Lou Maher (Computing…

An attribution statement identifies not only the presence of AI involvement, but also how AI was used. This approach makes important distinctions between different types and amounts of AI…

AI assistants are being created to help software engineers conduct a variety of coding-related tasks, such as writing, documenting, and testing code. We describe the use of the watsonx Code Assistant (WCA), an LLM-powered coding assistant deployed internally within IBM. Through surveys of two user cohorts (N=669) and unmoderated usability testing (N=15), we examined developers' experiences with WCA and its impact on their productivity. We learned about their motivations for using (or not using) WCA, we examined their expectations of its speed and quality, and we identified new considerations regarding ownership of and responsibility for generated code. Our case study characterizes the impact of an LLM-powered assistant on developers' perceptions of productivity and it shows that although such tools do often provide net productivity increases, these benefits may not always be experienced by all users.

by Kush Varshney (IBM Research, US)