An Artificial Intelligence Approach to Test Generation
An Artificial Intelligence Approach to Test Generation

Author: Narinder Singh

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 202

ISBN-13: 146131979X

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I am indebted to my thesis advisor, Michael Genesereth, for his guidance, inspiration, and support which has made this research possible. As a teacher and a sounding board for new ideas, Mike was extremely helpful in pointing out Haws, and suggesting new directions to explore. I would also like to thank Harold Brown for introducing me to the application of artificial intelligence to reasoning about designs, and his many valuable comments as a reader of this thesis. Significant contribu tions by the other members of my reading committee, Mark Horowitz, and Allen Peterson have greatly improved the content and organization of this thesis by forcing me to communicate my ideas more clearly. I am extremely grateful to the other members of the Logic Group at the Heuristic Programming Project for being a sounding board for my ideas, and providing useful comments. In particular, I would like to thank Matt Ginsberg, Vineet Singh, Devika Subramanian, Richard Trietel, Dave Smith, Jock Mackinlay, and Glenn Kramer for their pointed criticisms. This research was supported by Schlumberger Palo Alto Research (previously Fairchild Laboratory for Artificial Intelligence). I am grateful to Peter Hart, the former head of the AI lab, and his successor Marty Tenenbaum for providing an excellent environment for performing this research.

Artificial Intelligence Methods In Software Testing
Artificial Intelligence Methods In Software Testing

Author: Mark Last

Publisher: World Scientific

Published: 2004-06-03

Total Pages: 221

ISBN-13: 9814482609

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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.

Application of Artificial Intelligence to Assessment
Application of Artificial Intelligence to Assessment

Author: Hong Jiao

Publisher: IAP

Published: 2020-03-01

Total Pages: 218

ISBN-13: 1641139536

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The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers’ engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

Introduction to Advanced System-on-Chip Test Design and Optimization
Introduction to Advanced System-on-Chip Test Design and Optimization

Author: Erik Larsson

Publisher: Springer Science & Business Media

Published: 2006-03-30

Total Pages: 397

ISBN-13: 0387256245

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SOC test design and its optimization is the topic of Introduction to Advanced System-on-Chip Test Design and Optimization. It gives an introduction to testing, describes the problems related to SOC testing, discusses the modeling granularity and the implementation into EDA (electronic design automation) tools. The book is divided into three sections: i) test concepts, ii) SOC design for test, and iii) SOC test applications. The first part covers an introduction into test problems including faults, fault types, design-flow, design-for-test techniques such as scan-testing and Boundary Scan. The second part of the book discusses SOC related problems such as system modeling, test conflicts, power consumption, test access mechanism design, test scheduling and defect-oriented scheduling. Finally, the third part focuses on SOC applications, such as integrated test scheduling and TAM design, defect-oriented scheduling, and integrating test design with the core selection process.

Neural Models and Algorithms for Digital Testing
Neural Models and Algorithms for Digital Testing

Author: S.T. Chadradhar

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 187

ISBN-13: 1461539587

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 9 QUADRATIC 0-1 PROGRAMMING 8S 9. 1 Energy Minimization 86 9. 2 Notation and Tenninology . . . . . . . . . . . . . . . . . 87 9. 3 Minimization Technique . . . . . . . . . . . . . . . . . . 88 9. 4 An Example . . . . . . . . . . . . . . . . . . . . . . . . 92 9. 5 Accelerated Energy Minimization. . . . . . . . . . . . . 94 9. 5. 1 Transitive Oosure . . . . . . . . . . . . . . . . . 94 9. 5. 2 Additional Pairwise Relationships 96 9. 5. 3 Path Sensitization . . . . . . . . . . . . . . . . . 97 9. 6 Experimental Results 98 9. 7 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . 100 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 10 TRANSITIVE CLOSURE AND TESTING 103 10. 1 Background . . . . . . . . . . . . . . . . . . . . . . . . 104 10. 2 Transitive Oosure Definition 105 10. 3 Implication Graphs 106 10. 4 A Test Generation Algorithm 107 10. 5 Identifying Necessary Assignments 112 10. 5. 1 Implicit Implication and Justification 113 10. 5. 2 Transitive Oosure Does More Than Implication and Justification 115 10. 5. 3 Implicit Sensitization of Dominators 116 10. 5. 4 Redundancy Identification 117 10. 6 Summary 119 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 11 POLYNOMIAL-TIME TESTABILITY 123 11. 1 Background 124 11. 1. 1 Fujiwara's Result 125 11. 1. 2 Contribution of the Present Work . . . . . . . . . 126 11. 2 Notation and Tenninology 127 11. 3 A Polynomial TlDle Algorithm 128 11. 3. 1 Primary Output Fault 129 11. 3. 2 Arbitrary Single Fault 135 11. 3. 3 Multiple Faults. . . . . . . . . . . . . . . . . . . 137 11. 4 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . 139 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 ix 12 SPECIAL CASES OF HARD PROBLEMS 141 12. 1 Problem Statement 142 12. 2 Logic Simulation 143 12. 3 Logic Circuit Modeling . 146 12. 3. 1 Modelfor a Boolean Gate . . . . . . . . . . . . . 147 12. 3. 2 Circuit Modeling 148 12.

Knowledge-Based Software Engineering
Knowledge-Based Software Engineering

Author: Dorothy E. Setliff

Publisher: Springer Science & Business Media

Published: 2007-12-14

Total Pages: 105

ISBN-13: 058534714X

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Knowledge-Based Software Engineering brings together in one place important contributions and up-to-date research results in this important area. Knowledge-Based Software Engineering serves as an excellent reference, providing insight into some of the most important research issues in the field.

Principles of Testing Electronic Systems
Principles of Testing Electronic Systems

Author: Samiha Mourad

Publisher: John Wiley & Sons

Published: 2000-07-25

Total Pages: 444

ISBN-13: 9780471319313

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A pragmatic approach to testing electronic systems As we move ahead in the electronic age, rapid changes in technology pose an ever-increasing number of challenges in testing electronic products. Many practicing engineers are involved in this arena, but few have a chance to study the field in a systematic way-learning takes place on the job. By covering the fundamental disciplines in detail, Principles of Testing Electronic Systems provides design engineers with the much-needed knowledge base. Divided into five major parts, this highly useful reference relates design and tests to the development of reliable electronic products; shows the main vehicles for design verification; examines designs that facilitate testing; and investigates how testing is applied to random logic, memories, FPGAs, and microprocessors. Finally, the last part offers coverage of advanced test solutions for today's very deep submicron designs. The authors take a phenomenological approach to the subject matter while providing readers with plenty of opportunities to explore the foundation in detail. Special features include: * An explanation of where a test belongs in the design flow * Detailed discussion of scan-path and ordering of scan-chains * BIST solutions for embedded logic and memory blocks * Test methodologies for FPGAs * A chapter on testing system on a chip * Numerous references

Coordination of Distributed Problem Solvers
Coordination of Distributed Problem Solvers

Author: Edmund H. Durfee

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 278

ISBN-13: 1461316995

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As artificial intelligence (AI) is applied to more complex problems and a wider set of applications, the ability to take advantage of the computational power of distributed and parallel hardware architectures and to match these architec tures with the inherent distributed aspects of applications (spatial, functional, or temporal) has become an important research issue. Out of these research concerns, an AI subdiscipline called distributed problem solving has emerged. Distributed problem-solving systems are broadly defined as loosely-coupled, distributed networks of semi-autonomous problem-solving agents that perform sophisticated problem solving and cooperatively interact to solve problems. N odes operate asynchronously and in parallel with limited internode commu nication. Limited internode communication stems from either inherent band width limitations of the communication medium or from the high computa tional cost of packaging and assimilating information to be sent and received among agents. Structuring network problem solving to deal with consequences oflimited communication-the lack of a global view and the possibility that the individual agents may not have all the information necessary to accurately and completely solve their subproblems-is one of the major focuses of distributed problem-solving research. It is this focus that also is one of the important dis tinguishing characteristics of distributed problem-solving research that sets it apart from previous research in AI.

Artificial Intelligence in the Pacific Rim
Artificial Intelligence in the Pacific Rim

Author: Hozumi Tanaka

Publisher: IOS Press

Published: 1991

Total Pages: 1024

ISBN-13: 9789051990539

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In the last decade, AI firmly settled into our industrial society with the expert systems as the representative product. However, almost every one of the systems could cover only a single task domain. In the highly mechanized world of the 21st century, systems will become smart and user friendly enough to cover a wide range of task domains. Systems with much user friendliness must be multilingual because users in different domains usually have different languages. Language is formed in its own culture. Therefore, promotion for cross-cultural scientific interchange will be indispensable for the progress of AI.