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Machine Learning for Dynamic Software Analysis: Potentials and Limits - International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers (Paperback, 1st ed. 2018)
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Machine Learning for Dynamic Software Analysis: Potentials and Limits - International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers (Paperback, 1st ed. 2018)
Series: Lecture Notes in Computer Science, 11026
Expected to ship within 10 - 15 working days
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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 systems. These require new
software analysis techniques based on machine learning, such as
learning-based software testing, invariant generation or code
synthesis. Machine learning is a powerful paradigm that provides
novel approaches to automating the generation of models and other
essential software artifacts. This volume originates from a
Dagstuhl Seminar entitled "Machine Learning for Dynamic Software
Analysis: Potentials and Limits" held in April 2016. The seminar
focused on fostering a spirit of collaboration in order to share
insights and to expand and strengthen the cross-fertilisation
between the machine learning and software analysis communities. The
book provides an overview of the machine learning techniques that
can be used for software analysis and presents example applications
of their use. Besides an introductory chapter, the book is
structured into three parts: testing and learning, extension of
automata learning, and integrative approaches.
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