Titlebook: Machine Learning for Dynamic Software Analysis: Potentials and Limits; International Dagstu Amel Bennaceur,Reiner H?hnle,Karl Meinke Book 2

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Amel Bennaceur,Reiner H?hnle,Karl MeinkeWritten by international experts.Presents the state of the art and suggests new directions and collaborations for future research.Gives an overview of the machine learning techniques that can be used

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Machine Learning for Dynamic Software Analysis: Potentials and Limits978-3-319-96562-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Learning-Based Testing: Recent Progress and Future Prospectsrics enable a precise, general and quantitative approach to both speed of learning and test coverage. Moreover, quantitative approaches to black-box test coverage serve to distinguish LBT from alternative approaches such as random and search-based testing. We conclude by outlining some prospects for future research.

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Constraint-Based Behavioral Consistency of Evolving Software Systemsnd we describe some of the research challenges that must be solved. Our main idea is to combine software analysis approaches represented by various forms of static analysis and formal verification with runtime verification, monitoring, and automata learning in order to optimally leverage the de facto observed behaviour of the deployed systems.

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