Adaptive control approach for software quality improvement [[electronic resource] /] / editors, W. Eric Wong, Bojan Cukic

• Record Nr.: UNINA9910778809003321
• Pubbl/distr/stampa: New Jersey, : World Scientific, 2011
• ISBN: 1-283-43372-9; 9786613433725; 981-4340-92-8
• Descrizione fisica: 1 online resource (308 p.)
• Collana: Series on software engineering and knowledge engineering ; ; v. 20
• Altri autori (Persone): WongW. Eric; CukicBojan
• Disciplina: 005.14
• Soggetti: Software engineering; Computer software - Development
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographic references.
• Nota di contenuto: Preface; CONTENTS; 1. Prioritizing Coverage-Oriented Testing Process - An Adaptive-Learning-Based Approach and Case Study Fevzi Belli, Mubariz Eminov, Nida G ok ce and W. Eric Wong; 1. Introduction and Related Work; 2. Background; 2.1. Event Sequence Graphs; 2.2. Neural Network-Based Clustering; 3. Competitive Learning; 3.1. Distance-Based Competitive Learning Algorithm; 3.2. Angle-Based Competitive Learning Algorithm; 3.3. Adaptive Competitive Learning; Adaptive Competitive Learning Algorithm; 4. Prioritized ESG-Based Testing; 4.1. Definition of the Attributes of Events; 4.2. Definition of Importance Degree and PreferenceIndirect Determination of the Preference Degree; 5. A Case Study; 5.1. Derivation of the Test Cases; 5.2. Determination of Attributes of Events; 5.3. Construction of the Groups of Events; 5.4. Indirect Determination of Preference Degrees; 6. Conclusions and Future Work; References; 2. Statistical Evaluation Methods for V&V of Neuro-Adaptive Systems Y. Liu, J. Schumann and B. Cukic; 1. Introduction; 2. V&V of Neuro-Adaptive Systems; 2.1. Static V&V Approaches; 2.2. Dynamic V&V Approaches; 2.3. V&V of Neural Networks; 3. Statistical Evaluation of Neuro-Adaptive Systems3.1. Neural ; Network-Based Flight Control; 3.2. The Neural Networks; 3.2.1. Dynamic Cell Structure Network; 3.2.2. Sigma-Pi Neural Network; 3.3. Failure Detection Using Support Vector Data Description; 3.4. Evaluating Network's Learning Performance; 3.4.1. A Sensitivity Metric for DCS Networks; 3.4.2. A Sensitivity Metric for Sigma-Pi Networks; 3.5. Evaluating the Network's Output Quality; 3.5.1. Validity Index for DCS Networks; 3.5.2. Bayesian Confidence Tool for Sigma-Pi Networks; 4. Conclusions; References; 3. Adaptive Random Testing Dave Towey1. Introduction; 2. Adaptive Random Testing; 2.1. Distance-Based Adaptive Random Testing; 2.2. Restriction-Based Adaptive Random Testing; 2.3. Overheads; 2.4. Filtering; 2.5. Forgetting; 2.6. Mirror ART; 2.7. Probabilistic ART; 2.8. Fuzzy ART; 3. Summary; Acknowledgements; References; 4. Transparent Shaping: A Methodology for Adding Adaptive Behavior to Existing Software Systems and Applications S. Masoud Sadjadi, Philip K. Mckinley and Betty H.C. Cheng; 1. Introduction; 2. Basic Elements; 3. General Approach; 4. Middleware-Based Transparent Shaping; 4.1. ACT Architectural Overview4.2. ACT Core Components; Dynamic Interceptors; Proxies; Decision Makers; 4.3. ACT Operation; 4.4. ACT/J Implementation; 4.5. ACT/J Case Study; 5. Language-Based Transparent Shaping; 5.1. TRAP/J Architectural Overview; 5.2. TRAP/J Run-Time Model; 5.3. TRAP/J Case Study; Making ASA Adapt-Ready; Compile-Time Actions; Generated Aspect; Generated Wrapper-Level Class; Generated Metalevel Class; Adapting to Loss Rate; Balancing QoS and Energy Consumption; 6. Discussion; 7. Conclusions and Future Work; Acknowledgements; References; 5. Rule Extraction to Understand Changes in an Adaptive System Marjorie A. Darrah and Brian J. Taylor
• Sommario/riassunto: This book focuses on the topic of improving software quality using adaptive control approaches. As software systems grow in complexity, some of the central challenges include their ability to self-manage and adapt at run time, responding to changing user needs and environments, faults, and vulnerabilities. Control theory approaches presented in the book provide some of the answers to these challenges. The book weaves together diverse research topics (such as requirements engineering, software development processes, pervasive and autonomic computing, service-oriented architectures, on-line adaptation of software behavior, testing and QoS control) into a coherent whole. Written by world-renowned experts, this book is truly a noteworthy and authoritative reference for students, researchers and practitioners to better understand how the adaptive control approach can be applied to improve the quality of software systems. Book chapters also outline future theoretical and experimental challenges for researchers in this area. -- back cover.