Machine Learning for Dynamic Software Analysis: Potentials and Limits (Dagstuhl Seminar 16172)
Amel Bennaceur, Dimitra Giannakopoulou, Reiner Hähnle, Karl Meinke · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2016
This report documents the program and the outcomes of Dagstuhl Seminar 16172 "Machine Learning for Dynamic Software Analysis: Potentials and Limits". Machine learning is a powerful paradigm for software analysis that provides novel approaches to automating the generation of models and other essential artefacts. This Dagstuhl Seminar brought together top researchers active in the fields of machine learning and software analysis to have a better understanding of the synergies between these fields and suggest new directions and collaborations for future research.