Conferences  >  Informatics  >  Software Agents and Intelligent Agents in Artificial Intelligence  >  Germany

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Code search describes the process of retrieving source code from a repository, where that source code matches a query. Whether a developer is looking for where an error was thrown, learning how to use a new-to-them API, learning a new programming language, or browsing their team’s directory to familiarize themselves with the codebase, search underpins all these activities. Beyond those human-driven software engineering processes, search is also a component in automated software engineering, such as automated program repair, code example recommendation, and clone detection. Furthermore, new generative AI tools have challenged traditional code search by presenting alternative approaches to finding and reusing code.

This Dagstuhl Seminar brings together experts in mining software repositories, human factors in software engineering, software documentation, code examples, program analysis, and industrial code search systems to bridge the gap between industry and academia and set the roadmap for the next decade of code search research.

Software Engineering,    Database Technology and Design, Search Engines and Data Mining

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

While recommender systems is largely an applied field, their evaluation builds on and intersects theories from information retrieval, machine learning, and human-computer interaction. Historically, the theories and evaluation approaches in these fields are very different. Thoroughly evaluating recommender systems requires integrating all perspectives. Hence, this seminar will bring together experts from these fields and serve as a vehicle for discussing and developing the state-of-the-art and practice of evaluating recommender systems. The seminar will set the ground for developing recommender systems evaluation metrics, methods, and practices through collaborations and discussions between participants from diverse backgrounds, e.g., academic and industry researchers, industry practitioners, senior and junior. We emphasize the importance of getting and keeping the big picture of a recommender system’s performance in its context of use, for which it is ultimate to incorporate the technical and the human element. We will set the basis for the next generation of researchers, apt to evaluate and advance recommender systems thoroughly.

Application Software,    Human-Computer Interaction, User Experience