DagRep.6.4.161.pdf
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Machine Learning for Dynamic Software Analysis: Potentials and Limits (Dagstuhl Seminar 16172)
Authors and all authors of the abstracts in this report
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Issue:
Dagstuhl Reports, Volume 6, Issue 4
Volume: Dagstuhl Reports, Volume 6
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication date: 2016-10-17
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Amel Bennaceur, Dimitra Giannakopoulou, Reiner Hähnle, and Karl Meinke. Machine Learning for Dynamic Software Analysis: Potentials and Limits (Dagstuhl Seminar 16172). In Dagstuhl Reports, Volume 6, Issue 4, pp. 161-173, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)
https://doi.org/10.4230/DagRep.6.4.161
https://doi.org/10.4230/DagRep.6.4.161
Abstract
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.
Keywords
- Machine learning
- Automata learning
- Software analysis
- Dynamic analysis
- Testing
- Model extraction
- Systems integration
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