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| artificial intelligence methods in software testing: Artificial Intelligence Methods in Software Testing Horst Bunke, Mark Last, Abraham Kandel, 2004 An inadequate infrastructure for software testing is causing major losses to the world economy. The characteristics of software quality problems are quite similar to other tasks successfully tackled by artificial intelligence techniques. The aims of this book are to present state-of-the-art applications of artificial intelligence and data mining methods to quality assurance of complex software systems, and to encourage further research in this important and challenging area. Contents: Fuzzy CauseOCoEffect Models of Software Testing (W Pedrycz & G Vukovich); Black-Box Testing with Info-Fuzzy Networks (M Last & M Friedman); Automated GUI Regression Testing Using AI Planning (A M Memon); Test Set Generation and Reduction with Artificial Neural Networks (P Saraph et al.); Three-Group Software Quality Classification Modeling Using an Automated Reasoning Approach (T M Khoshgoftaar & N Seliya); Data Mining with Resampling in Software Metrics Databases (S Dick & A Kandel). Readership: Students, researchers and professionals in computer science, information systems, software testing and data mining. |
| artificial intelligence methods in software testing: Artificial Intelligence Methods for Software Engineering Meir Kalech, Rui Abreu, Mark Last, 2021 |
| artificial intelligence methods in software testing: Artificial Intelligence Methods For Software Engineering Meir Kalech, Rui Abreu, Mark Last, 2021-06-15 Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s) |
| artificial intelligence methods in software testing: Artificial Intelligence Methods in Software Testing Mark Last, Abraham Kandel, Horst Bunke, 2004 - Coverage of novel methods for software testing and software quality assurance - Introduction to state-of-the-art data mining models and techniques - Analyses of new and promising application domains of artificial intelligence and data mining in software quality engineering - Contributions from leading authors in the fields of software engineering and data mining. |
| artificial intelligence methods in software testing: Artificial Intelligence Methods for Optimization of the Software Testing Process Sahar Tahvili, Leo Hatvani, 2022-07-26 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 |
| artificial intelligence methods in software testing: Artificial Intelligence Methods for Optimization of the Software Testing Process Sahar Tahvili, Leo Hatvani, 2022-07-21 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 |
| artificial intelligence methods in software testing: Machine Learning Applications In Software Engineering Du Zhang, Jeffrey J P Tsai, 2005-02-21 Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area. |
| artificial intelligence methods in software testing: Computational Intelligence in Software Engineering Witold Pedrycz, James F. Peters, 1998 This unique volume is the first publication on software engineering and computational intelligence (CI) viewed as a synergistic interplay of neurocomputing, granular computation (including fuzzy sets and rough sets), and evolutionary methods. It presents a unified view of CI in the context of software engineering. The book addresses a number of crucial issues: what is CI, what role does it play in software development, how are CI elements built into successive phases of the software life cycle, and what is the role played by CI in quantifying fundamental features of software artifacts? With contributions from leading researchers and practitioners, the book provides the reader with a wealth of new concepts and approaches, complete algorithms, in-depth case studies, and thought-provoking exercises. The topics coverage include neurocomputing, granular as well as evolutionary computing, object-oriented analysis and design in software engineering. There is also an extensive bibliography. |
| artificial intelligence methods in software testing: Handbook on Artificial Intelligence-Empowered Applied Software Engineering Maria Virvou, George A. Tsihrintzis, Nikolaos G. Bourbakis, Lakhmi C. Jain, 2022-09-03 This book provides a structured overview of artificial intelligence-empowered applied software engineering. Evolving technological advancements in big data, smartphone and mobile software applications, the Internet of Things and a vast range of application areas in all sorts of human activities and professions lead current research towards the efficient incorporation of artificial intelligence enhancements into software and the empowerment of software with artificial intelligence. This book at hand, devoted to Novel Methodologies to Engineering Smart Software Systems Novel Methodologies to Engineering Smart Software Systems, constitutes the first volume of a two-volume Handbook on Artificial Intelligence-empowered Applied Software Engineering. Topics include very significant advances in (i) Artificial Intelligence-Assisted Software Development and (ii) Software Engineering Tools to develop Artificial Intelligence Applications, as well as a detailed Survey of Recent Relevant Literature. Professors, researchers, scientists, engineers and students in artificial intelligence, software engineering and computer science-related disciplines are expected to benefit from it, along with interested readers from other disciplines. |
| artificial intelligence methods in software testing: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| artificial intelligence methods in software testing: Artificial Intelligence Methods and Applications Nikolaos G. Bourbakis, 1992 This volume is the first in a series which deals with the challenge of AI issues, gives updates of AI methods and applications, and promotes high quality new ideas, techniques and methodologies in AI. This volume contains articles by 38 specialists in various AI subfields covering theoretical and application issues. |
| artificial intelligence methods in software testing: Computational Intelligence in Software Modeling Vishal Jain, Jyotir Moy Chatterjee, Ankita Bansal, Utku Kose, Abha Jain, 2022-02-21 Researchers, academicians and professionals expone in this book their research in the application of intelligent computing techniques to software engineering. As software systems are becoming larger and complex, software engineering tasks become increasingly costly and prone to errors. Evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, and others techniques can help the effi ciency of software engineering. |
| artificial intelligence methods in software testing: Instant Approach to Software Testing Nayyar Dr. Anand, 2019-11-04 One-stop Guide to software testing types, software errors, and planning process Key featuresa- Presents a comprehensive investigation about the software testing approach in terms of techniques, tools and standardsa- Highlights test case development and defect trackinga- In-depth coverage of test reports developmenta- Covers the Selenium testing tool in detaila- Comprehensively covers IEEE/ISO/IEC software testing standardsDescriptionSoftware testing is conducted to assist testers with information to improvise the quality of the product under testing. The book primarily aims to present testing concepts, principles, practices, methods cum approaches used in practice. The book will help the readers to learn and detect faults in software before delivering it to the end user. The book is a judicious mix of software testing concepts, principles, methodologies, and tools to undertake a professional course in software testing. The book will be a useful resource for students, academicians, industry experts, and software architects to learn artefacts of testing. Book discuss the foundation and primary aspects connected to the world of software testing, then it discusses the levels, types and terminologies associated with software testing. In the further chapters it will gives a comprehensive overview of software errors faced in software testing as well as various techniques for error detection, then the test case development and security testing. In the last section of the book discusses the defect tracking, test reports, software automation testing using the Selenium tool and then ISO/IEEE-based software testing standards. What will you learn Taxonomy, principles and concepts connected to software testing. Software errors, defect tracking, and the entire testing process to create quality products. Generate test cases and reports for detecting errors, bugs, and faults. Automation testing using the Selenium testing tool. Software testing standards as per IEEE/ISO/IEC to conduct standard and quality testing. Who this book is forThe readers should have a basic understanding of software engineering concepts, object-oriented programming and basic programming fundamentals. Table of contents1. Introduction to Software Testing2. Software Testing Levels, Types, Terms, and Definitions3. Software Errors4. Test Planning Process (According to IEEE standard 829)5. Test Case Development6. Defect Tracking7. Types of Test Reports8. Software Test Automation9. Understanding the Software Testing Standards About the authorDr Anand Nayyar received PhD (Computer Science) in the field of Wireless Sensor Networks. He is currently working in Graduate School, Duy Tan University, Da Nang, Vietnam. A certified professional with 75+ professional certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam, and many more. He has published more than 250 research papers in various National and International Conferences, International Journals (Scopus/SCI/SCIE/SSCI Indexed). He is a member of more than 50+ associations as a senior and life member and also acts as an ACM Distinguished Speaker. He is currently working in the area of Wireless Sensor Networks, MANETS, Swarm Intelligence, Cloud Computing, Internet of Things, Blockchain, Machine Learning, Deep Learning, Cyber Security, Network Simulation, and Wireless Communications. His Blog links: http://www.anandnayyar.comHis LinkedIn Profile: https://in.linkedin.com/in/anandnayyar |
| artificial intelligence methods in software testing: Machine Learning: Concepts, Methodologies, Tools and Applications Management Association, Information Resources, 2011-07-31 This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets--Provided by publishe |
| artificial intelligence methods in software testing: Human-Centered AI Ben Shneiderman, 2022-01-13 The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity. |
| artificial intelligence methods in software testing: 2018 4th International Conference on Computing Communication and Automation (ICCCA) IEEE Staff, 2018-12-14 School of Computing Science & Engineering of Galgotias University Uttar Pradesh, invites you to associate with us for upcoming conference, ICCCA2018, a two day International Conference to be held on December 14 15, 2018 ICCCA2018 International Conference on Computing, Communication and Automation team with pleasure invites you to contribute with original research papers, to this blind and peer reviewed conference |
| artificial intelligence methods in software testing: Accelerating Software Quality Eran Kinsbruner, 2020-08-10 The book Accelerating Software Quality: Machine Learning and Artificial Intelligence in the Age of DevOps is a complete asset for software developers, testers, and managers that are on their journey to a more mature DevOps workflow, and struggle with better automation and data-driven decision making. DevOps is a mature process across the entire market, however, with existing Non-AI/ML technologies and models, it comes short in expediting release cycle, identifying productivity gaps and addressing them. This book, that was implemented by myself with the help of leaders from the DevOps and test automation space, is covering topics from basic introduction to AI and ML in software development and testing, implications of AI and ML on existing apps, processes, and tools, practical tips in applying commercial and open-source AI/ML tools within existing tool chain, chat-bots testing, visual based testing using AI, automated security scanning for vulnerabilities, automated code reviews, API testing and management using AI/ML, reducing effort and time through test impact analysis (TIA), robotic process automation (RPA), AIOps for smarter code deployments and production defects prevention, and many more.When properly leveraging such tools, DevOps teams can benefit from greater code quality and functional and non-functional test automation coverage. This increases their release cycle velocity, reduces noise and software waste, and enhances their app quality.The book is divided into 3 main sections: *Section 1 covers the fundamentals of AI and ML in software development and testing. It includes introductions, definitions, 101 for testing AI-Based applications, classifications of AI/ML and defects that are tied to AI/ML, and more.*Section 2 focuses on practical advises and recommendations for using AI/ML based solutions within software development activities. This section includes topics like visual AI test automation, AI in test management, testing conversational AI applications, RPA benefits, API testing and much more.*Section 3 covers the more advanced and future-looking angles of AI and ML with projections and unique use cases. Among the topics in this section are AI and ML in logs observability, AIOps benefits to an entire DevOps teams, how to maintain AI/ML test automation, Test impact analysis with AI, and more.The book is packed with many proven best practices, real life examples, and many other open source and commercial solution recommendations that are set to shape the future of DevOps together with ML/AI |
| artificial intelligence methods in software testing: Formal Methods in Artificial Intelligence Allan Ramsay, 1988 This book covers the background of classical logic, including the major meta-theorems, and the state of the art in theorem proving. |
| artificial intelligence methods in software testing: Deterministic Artificial Intelligence Timothy Sands, 2020-05-27 Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book. |
| artificial intelligence methods in software testing: Artificial Intelligence and Software Engineering Derek Partridge, 2013-04-11 Managers, business owners, computer literate individuals, software developers, students, and researchers--all are looking for an understanding of artificial intelligence (AI) and what might be in the future. In this literate yet easy-to-read discussion, Derek Partridge explains what artificial intelligence can and cannot do, and what it holds for applications such as banking, financial services, and expert systems of all kinds. Topics include: the strengths and weaknesses of software development and engineering; machine learning and its promises and problems; expert systems and success stories; and practical software through artificial intelligence. |
| artificial intelligence methods in software testing: Bio-Inspired Artificial Intelligence Dario Floreano, Claudio Mattiussi, 2008-08-22 A comprehensive introduction to new approaches in artificial intelligence and robotics that are inspired by self-organizing biological processes and structures. New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligence—to mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systems—including several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading. |
| artificial intelligence methods in software testing: Effective Methods for Software Testing, CafeScribe William E. Perry, 2007-03-31 Written by the founder and executive director of the Quality Assurance Institute, which sponsors the most widely accepted certification program for software testing Software testing is a weak spot for most developers, and many have no system in place to find and correct defects quickly and efficiently This comprehensive resource provides step-by-step guidelines, checklists, and templates for each testing activity, as well as a self-assessment that helps readers identify the sections of the book that respond to their individual needs Covers the latest regulatory developments affecting software testing, including Sarbanes-Oxley Section 404, and provides guidelines for agile testing and testing for security, internal controls, and data warehouses CD-ROM with all checklists and templates saves testers countless hours of developing their own test documentation Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file. |
| artificial intelligence methods in software testing: Machine Learning Engineering in Action Ben Wilson, 2022-04-26 Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the Technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. . |
| artificial intelligence methods in software testing: 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) IEEE Staff, 2020-06-24 CISTI is a technical and scientific event, whose purpose is to present and discuss knowledge, new perspectives, experiences and innovations in the Information Systems and Technologies field |
| artificial intelligence methods in software testing: Trends in Software Testing Hrushikesha Mohanty, J. R. Mohanty, Arunkumar Balakrishnan, 2016-07-26 This book is focused on the advancements in the field of software testing and the innovative practices that the industry is adopting. Considering the widely varied nature of software testing, the book addresses contemporary aspects that are important for both academia and industry. There are dedicated chapters on seamless high-efficiency frameworks, automation on regression testing, software by search, and system evolution management. There are a host of mathematical models that are promising for software quality improvement by model-based testing. There are three chapters addressing this concern. Students and researchers in particular will find these chapters useful for their mathematical strength and rigor. Other topics covered include uncertainty in testing, software security testing, testing as a service, test technical debt (or test debt), disruption caused by digital advancement (social media, cloud computing, mobile application and data analytics), and challenges and benefits of outsourcing. The book will be of interest to students, researchers as well as professionals in the software industry. |
| artificial intelligence methods in software testing: Unit Testing Principles, Practices, and Patterns Vladimir Khorikov, 2020-01-06 This book is an indispensable resource. - Greg Wright, Kainos Software Ltd. Radically improve your testing practice and software quality with new testing styles, good patterns, and reliable automation. Key Features A practical and results-driven approach to unit testing Refine your existing unit tests by implementing modern best practices Learn the four pillars of a good unit test Safely automate your testing process to save time and money Spot which tests need refactoring, and which need to be deleted entirely Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Great testing practices maximize your project quality and delivery speed by identifying bad code early in the development process. Wrong tests will break your code, multiply bugs, and increase time and costs. You owe it to yourself—and your projects—to learn how to do excellent unit testing. Unit Testing Principles, Patterns and Practices teaches you to design and write tests that target key areas of your code including the domain model. In this clearly written guide, you learn to develop professional-quality tests and test suites and integrate testing throughout the application life cycle. As you adopt a testing mindset, you’ll be amazed at how better tests cause you to write better code. What You Will Learn Universal guidelines to assess any unit test Testing to identify and avoid anti-patterns Refactoring tests along with the production code Using integration tests to verify the whole system This Book Is Written For For readers who know the basics of unit testing. Examples are written in C# and can easily be applied to any language. About the Author Vladimir Khorikov is an author, blogger, and Microsoft MVP. He has mentored numerous teams on the ins and outs of unit testing. Table of Contents: PART 1 THE BIGGER PICTURE 1 ¦ The goal of unit testing 2 ¦ What is a unit test? 3 ¦ The anatomy of a unit test PART 2 MAKING YOUR TESTS WORK FOR YOU 4 ¦ The four pillars of a good unit test 5 ¦ Mocks and test fragility 6 ¦ Styles of unit testing 7 ¦ Refactoring toward valuable unit tests PART 3 INTEGRATION TESTING 8 ¦ Why integration testing? 9 ¦ Mocking best practices 10 ¦ Testing the database PART 4 UNIT TESTING ANTI-PATTERNS 11 ¦ Unit testing anti-patterns |
| artificial intelligence methods in software testing: Applications of Artificial Intelligence in Process Systems Engineering Jingzheng Ren, Weifeng Shen, Yi Man, Lichun Dong, 2021-06-17 Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering |
| artificial intelligence methods in software testing: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning. |
| artificial intelligence methods in software testing: Cross-disciplinary Applications of Artificial Intelligence and Pattern Recognition V. K. Mago, Nitin Bhatia, 2012 This book provides a common platform for researchers to present theoretical and applied research findings for enhancing and developing intelligent systems, discussing advances in and applications of pattern recognition technologies and artificial intelligence-- |
| artificial intelligence methods in software testing: Machine Learning, ECML- ... , 2004 |
| artificial intelligence methods in software testing: Advanced Automated Software Testing Izzat Alsmadi, 2012-01-01 This book discusses the current state of test automation practices, as it includes chapters related to software test automation and its validity and applicability in different domains--Provided by publisher. |
| artificial intelligence methods in software testing: Developer Testing Alexander Tarlinder, 2016-09-07 How do successful agile teams deliver bug-free, maintainable software—iteration after iteration? The answer is: By seamlessly combining development and testing. On such teams, the developers write testable code that enables them to verify it using various types of automated tests. This approach keeps regressions at bay and prevents “testing crunches”—which otherwise may occur near the end of an iteration—from ever happening. Writing testable code, however, is often difficult, because it requires knowledge and skills that cut across multiple disciplines. In Developer Testing, leading test expert and mentor Alexander Tarlinder presents concise, focused guidance for making new and legacy code far more testable. Tarlinder helps you answer questions like: When have I tested this enough? How many tests do I need to write? What should my tests verify? You’ll learn how to design for testability and utilize techniques like refactoring, dependency breaking, unit testing, data-driven testing, and test-driven development to achieve the highest possible confidence in your software. Through practical examples in Java, C#, Groovy, and Ruby, you’ll discover what works—and what doesn’t. You can quickly begin using Tarlinder’s technology-agnostic insights with most languages and toolsets while not getting buried in specialist details. The author helps you adapt your current programming style for testability, make a testing mindset “second nature,” improve your code, and enrich your day-to-day experience as a software professional. With this guide, you will Understand the discipline and vocabulary of testing from the developer’s standpoint Base developer tests on well-established testing techniques and best practices Recognize code constructs that impact testability Effectively name, organize, and execute unit tests Master the essentials of classic and “mockist-style” TDD Leverage test doubles with or without mocking frameworks Capture the benefits of programming by contract, even without runtime support for contracts Take control of dependencies between classes, components, layers, and tiers Handle combinatorial explosions of test cases, or scenarios requiring many similar tests Manage code duplication when it can’t be eliminated Actively maintain and improve your test suites Perform more advanced tests at the integration, system, and end-to-end levels Develop an understanding for how the organizational context influences quality assurance Establish well-balanced and effective testing strategies suitable for agile teams |
| artificial intelligence methods in software testing: Artificial Intelligence David L. Poole, Alan K. Mackworth, 2017-09-25 Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains. |
| artificial intelligence methods in software testing: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| artificial intelligence methods in software testing: Testing Computer Software Cem Kaner, Jack Falk, Hung Q. Nguyen, 1999-04-26 Dies ist die 2. Auflage eines herausragenden und äußerst erfolgreichen Softwaretitels, der auch von Amazon besonders empfohlen wird. Früher herausgegeben von VNR Computer Library, ist dieses Buch jetzt bei Wiley erhältlich. Zuverlässige Computer-Software ist der Schlüssel zum Erfolg aller IT-Unternehmen und -systeme. Jedoch ist es unmöglich erfolgreiche und zuverlässige Software herzustellen, ohne daß diese ein umfangreiches Testverfahren durchläuft. Und genau um diese Testverfahren geht es hier. Cem Kaner, anerkannter Experte auf diesem Gebiet, hat mit diesem Buch einen Leitfaden verfaßt, der von unschätzbarem Wert ist für ALLE: Für Studenten, die sich um eine Stelle als Software-Tester bewerben, für erfahrene Programmierer, die Fehler schnell aufdecken müssen oder mit einer Armada von Testern kommunizieren müssen und für Projekt- und Test-Manager, die eine Vielzahl von Leuten, Fristen und Erwartungen jedes einzelnen Softwareprojekts unter einen Hut kriegen müssen. Außerdem ist dieses Buch eine große Hilfe für alle, die ein Betriebssystem für den Privatgebrauch erworben haben, das nicht ihren Erwartungen entspricht. Der Erfolg dieses Buches beruht auf seiner Realitätsnähe und Praxisbezogenheit: Qualität und Zuverlässigkeit von Software am modernen Arbeitsplatz. (y08/99) |
| artificial intelligence methods in software testing: A Practitioner's Guide to Software Test Design Lee Copeland, 2004 Written by a leading expert in the field, this unique volume contains current test design approaches and focuses only on software test design. Copeland illustrates each test design through detailed examples and step-by-step instructions. |
| artificial intelligence methods in software testing: Computer Aided Verification Isil Dillig, Serdar Tasiran, 2019-07-12 This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency. |
| artificial intelligence methods in software testing: Computational Intelligence in Software Quality Assurance Scott Dick, Abraham Kandel, 2005 Software systems surround us. Software is a critical component in everything from the family car through electrical power] systems to military equipment. As software ploys an ever-increasing role in our lives and livelihoods, the quality of that software becomes more and more critical. However, our ability to deliver high-quality software has not kept up with those increasing demands. The economic fallout is enormous; the US economy alone is losing over US$50 billion per year due to software failures. This book presents new research into using advanced artificial intelligence techniques to guide software quality improvements. The techniques of chaos theory and data mining arc brought to bear to provide new insights into the software development process. Written for researchers and practitioners in software engineering and computational intelligence, this book is a unique and important bridge between these two fields. |
| artificial intelligence methods in software testing: Industrial Software Technology Institution of Electrical Engineers, 1987 This book brings together twenty one papers on developments in software technology, selected from papers presented at recent European Seminars on Industrial Software Technology organised by the European Workshop on Industrial Computer Systems (EWICS). |
| artificial intelligence methods in software testing: Emerging Artificial Intelligence Applications in Computer Engineering Ilias G. Maglogiannis, 2007 Provides insights on how computer engineers can implement artificial intelligence (AI) in real world applications. This book presents practical applications of AI. |
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
artificial - definition and meaning - Wordnik
Made or contrived by art, or by human skill and labor: opposed to natural: as, artificial heat or light; an artificial magnet. Made in imitation of or as a substitute for that which is natural or real: …
Artificial Definition & Meaning - YourDictionary
Made by human work or art, not by nature; not natural. Not arising from natural or necessary causes; contrived or arbitrary. Made in imitation of or as a substitute for something natural; …
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
artificial - definition and meaning - Wordnik
Made or contrived by art, or by human skill and labor: opposed to natural: as, artificial heat or light; an artificial magnet. Made in imitation of or as a substitute for that which is natural or real: …
Artificial Definition & Meaning - YourDictionary
Made by human work or art, not by nature; not natural. Not arising from natural or necessary causes; contrived or arbitrary. Made in imitation of or as a substitute for something natural; …
