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007			cr nn 008mamaa
008			220601s2020 sz | s |||| 0|eng d
020			_a9783031025518
024	7		_a10.1007/978-3-031-02551-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.758 _b2020 EB
100	1		_aPăsăreanu, Corina S. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687648
245	1	0	_aSymbolic Execution and Quantitative Reasoning : _bApplications to Software Safety and Security _cby Corina S. Păsăreanu
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (IX, 65 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Software Engineering _x2328-3327
505	0		_aAcknowledgments -- Introduction -- Symbolic Execution: The Basics -- Symbolic Complexity Analysis -- Probabilistic Reasoning -- Side-Channel Analysis -- Conclusion and Directions for the Future -- Bibliography -- Author's Biography.
520			_aThis book reviews recent advances in symbolic execution and its probabilistic variant and discusses how they can be used to ensure the safety and security of software systems. Symbolic execution is a systematic program analysis technique which explores multiple program behaviors all at once by collecting and solving symbolic constraints collected from the branching conditions in the program. The obtained solutions can be used as test inputs that execute feasible program paths. Symbolic execution has found many applications in various domains, such as security, smartphone applications, operating systems, databases, and more recently deep neural networks, uncovering subtle errors and unknown vulnerabilities. We review here the technique has also been extended to reason about algorithmic complexity and resource consumption. Furthermore, symbolic execution has been recently extended with probabilistic reasoning, allowing one to reason about quantitative properties of software systems. The approach computes the conditions to reach target program events of interest and uses model counting to quantify the fraction of the input domain satisfying these conditions thus computing the probability of event occurrence. This probabilistic information can be used for example to compute the reliability of an aircraft controller under different wind conditions (modeled probabilistically) or to quantify the leakage of sensitive data in a software system, using information theory metrics such as Shannon entropy. This book is intended for students and software engineers who are interested in advanced techniques for testing and verifying software systems.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9159166 _aDistribución (Teoría de probabilidades)
650		7	_2embne _9152630 _aIngeniería del software
650		7	_2embne _9158200 _aSeguridad informática
776	0	8	_iPrinted edition: _z9783031003417
776	0	8	_iPrinted edition: _z9783031014239
776	0	8	_iPrinted edition: _z9783031036798
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02551-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI