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Record Nr. |
UNINA9910616360803321 |
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Titolo |
Optimization of Automated Software Testing Using Meta-Heuristic Techniques / / edited by Manju Khari, Deepti Bala Mishra, Biswaranjan Acharya, Ruben Gonzalez Crespo |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (189 pages) |
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Collana |
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EAI/Springer Innovations in Communication and Computing, , 2522-8609 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Data mining |
Engineering - Data processing |
Computational Intelligence |
Data Mining and Knowledge Discovery |
Data Engineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Introduction -- Testing Fundamentals -- Strategies and Methods For Test Cases -- Automated Testing Levels -- Polices and Plans for Testing -- Test Organization -- Controlling and Monitoring Testing Process -- Evaluating Automated Software Quality -- Defect Analysis and Prevention -- Optimization -- Security Testing -- Conclusion. |
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Sommario/riassunto |
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This book provides awareness of different evolutionary methods used for automatic generation and optimization of test data in the field of software testing. While the book highlights on the foundations of software testing techniques, it also focuses on contemporary topics for research and development. This book covers the automated process of testing in different levels like unit level, integration level, performance level, evaluation of testing strategies, testing in security level, optimizing test cases using various algorithms, and controlling and monitoring the testing process etc. This book aids young researchers in the field of optimization of automated software testing, provides |
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academics with knowledge on the emerging field of AI in software development, and supports universities, research centers, and industries in new projects using AI in software testing. Supports the advancement in the artificial intelligence used in software development; Advances knowledge on artificial intelligence based metaheuristic approach in software testing; Encourages innovation in traditional software testing field using recent artificial intelligence. ยท . |
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