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003			ES-MaUEC
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008			230324s2023 sz | o |||| 0|eng d
020			_a9783031220579
024	7		_a10.1007/978-3-031-22057-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA345 _b2023 EB
100	1		_aParsa, Saeed _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689527
245	1	0	_aSoftware Testing Automation : _bTestability Evaluation, Refactoring, Test Data Generation and Fault Localization _cby Saeed Parsa
250			_a1st ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aSoftware Testability -- Unit testing and Test-Driven Development -- Acceptance Testing and Behavior Driven Development.
520			_aThis book is about the design and development of tools for software testing. It intends to get the reader involved in software testing rather than simply memorizing the concepts. The source codes are downloadable from the book website. The book has three parts: software testability, fault localization, and test data generation. Part I describes unit and acceptance tests and proposes a new method called testability-driven development (TsDD) in support of TDD and BDD. TsDD uses a machine learning model to measure testability before and after refactoring. The reader will learn how to develop the testability prediction model and write software tools for automatic refactoring. Part II focuses on developing tools for automatic fault localization. This part shows the reader how to use a compiler generator to instrument source code, create control flow graphs, identify prime paths, and slice the source code. On top of these tools, a software tool, Diagnoser, is offered to facilitate experimenting with and developing new fault localization algorithms. Diagnoser takes a source code and its test suite as input and reports the coverage provided by the test cases and the suspiciousness score for each statement. Part III proposes using software testing as a prominent part of the cyber-physical system software to uncover and model unknown physical behaviors and the underlying physical rules. The reader will get insights into developing software tools to generate white box test data.
988			_aSpringer_Computer_2023
650		7	_2embne _9143900 _aDiseño asistido por ordenador
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-22057-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ek _zSI