Sahar Tahvili – författare
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5 produkter
5 produkter
E-bok
Engelska, 20222 108 kr
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Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way.As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how''s, what''s and whys.To learn more about Elsevier''s Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence- Presents one of the first empirical studies in the field, contrasting theoretical assumptions on innovations in a real industrial environment with a large set of use cases from developed and developing testing processes at various large industries- Explores specific comparative methodologies, focusing on developed and developing AI-based solutions- Serves as a guideline for conducting industrial research in the artificial intelligence and software testing domain- Explains all proposed solutions through real industrial case studies
Häftad, Engelska, 2022
1 483 kr
Skickas inom 10-15 vardagar
Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way.
As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys.
To learn more about Elsevier's Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence�
Presents one of the first empirical studies in the field, contrasting theoretical assumptions on innovations in a real industrial environment with a large set of use cases from developed and developing testing processes at various large industries Explores specific comparative methodologies, focusing on developed and developing AI-based solutions Serves as a guideline for conducting industrial research in the artificial intelligence and software testing domain Explains all proposed solutions through real industrial case studies
E-bok
PDF, Engelska, 201415 kr
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Master's Thesis from the year 2014 in the subject Mathematics - Applied Mathematics, grade: A+, Malardalen University, language: English, abstract: The main goal of this project is to explore the use of stochastic simulation, genetic algorithms, fuzzy decision making and other tools for solving complex maintenance planning optimization problems. We use two different maintenance activities, corrective maintenance and preventive maintenance. Since the evaluation of specifc candidate maintenance policies can take a long time to execute and the problem of finding the optimal policy is both non-linear and non-convex, we propose the use of genetic algorithms (GA) for the optimization. The main task of the GA is to find the optimal maintenance policy, which involves: (1) the probability of breakdown, (2) calculation of the cost of corrective maintenance, (3) calculation of the cost of preventive maintenance and (4) calculation of ROI (Return On Investment). Another goal of this project is to create a decision making model for multi-criteria systems. To find a near-optimal maintenance policy, we need to have an overview over the health status of the system components. To model the health of a component we should find all the operational criteria that affect it. We also need to analyze alternative maintenance activities in order to make the best maintenance decisions. In order to do that, the TOPSIS method and fuzzy decision making has been used. To evaluate the proposed methodology, internal combustion engine cooling of a typical Scania truck was used as a case study.
Häftad, Engelska, 2014
838 kr
Skickas inom 3-6 vardagar
E-bok
PDF, Engelska, 201916 kr
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Doctoral Thesis / Dissertation from the year 2018 in the subject Computer Science - Software, grade: Excellence, Malardalen University (The school of Innovation, design and engineering), language: English, abstract: Imagine a world where software glitches are relics of the past, where embedded systems operate with unwavering reliability. This vision fuels the groundbreaking research presented within these pages, a deep dive into the optimization of software testing, specifically targeting the intricate realm of integration testing. Prepare to discover a novel approach to conquering the challenges of redundant test execution failures and the ever-present quest for comprehensive requirement coverage. This isn't just another academic exercise; it's a pragmatic solution born from real-world complexities. Explore the development and deployment of cutting-edge tools meticulously designed to predict and schedule test executions with unparalleled efficiency. Witness how dependency analysis between integration test cases unlocks opportunities to streamline testing efforts, ensuring maximum impact with minimal resource expenditure. Journey alongside the author as they navigate the intricacies of multi-criteria decision-making, expertly balancing factors like test execution time and requirement coverage to achieve optimal testing strategies. Uncover the power of ESPRET and sOrTES, innovative methods rigorously tested and validated at Bombardier Transportation, demonstrating significant strides in reducing wasted testing cycles and enhancing overall software quality. Delve into the core of software testing optimization, where the fusion of theoretical insights and practical application paves the way for a future of flawless software performance. This book offers not only a comprehensive analysis of existing techniques but also provides a blueprint for implementing transformative changes in software development workflows. Discover how to leverage decision support systems to prioritize critical test cases, ensuring that every line of code is scrutinized with the utmost diligence. Grasp the fundamental principles of test scheduling and learn how to orchestrate testing activities for maximum efficiency and effectiveness. Whether you're a seasoned software engineer, a quality assurance specialist, or a student eager to master the art of software testing, this book provides invaluable insights and practical guidance for navigating the complexities of modern software development. Unlock the secrets to building robust and reliable embedded systems, one meticulously optimized test case at a time. Software Testing, Optimization, Integration Testing, Decision Support System, Dependency, Test Scheduling, and Requirement Coverage are all explored in depth, offering a holistic view of the software testing landscape and providing actionable strategies for achieving unparalleled software quality.