Machine Learning for Dynamic Software Analysis: Potentials and Limits International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Bennaceur, Amel (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Hähnle, Reiner (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Meinke, Karl (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Programming and Software Engineering ; 11026
• Θέματα: Software engineering.; Artificial intelligence.; Computers.; Software Engineering/Programming and Operating Systems.; Artificial Intelligence.; Theory of Computation.

Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software sy...

Πίνακας περιεχομένων:

• Introduction
• Testing and Learning
• Extensions of Automata Learning
• Integrative Approaches.