LEADER 06046nam 2200685 a 450 001 9910457436603321 005 20200520144314.0 010 $a1-283-43372-9 010 $a9786613433725 010 $a981-4340-92-8 035 $a(CKB)2550000000079522 035 $a(EBL)840648 035 $a(OCoLC)858228204 035 $a(SSID)ssj0000644698 035 $a(PQKBManifestationID)12206455 035 $a(PQKBTitleCode)TC0000644698 035 $a(PQKBWorkID)10676400 035 $a(PQKB)10206208 035 $a(MiAaPQ)EBC840648 035 $a(WSP)00008099 035 $a(Au-PeEL)EBL840648 035 $a(CaPaEBR)ebr10524646 035 $a(CaONFJC)MIL343372 035 $a(EXLCZ)992550000000079522 100 $a20110928d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdaptive control approach for software quality improvement$b[electronic resource] /$feditors, W. Eric Wong, Bojan Cukic 210 $aNew Jersey $cWorld Scientific$d2011 215 $a1 online resource (308 p.) 225 1 $aSeries on software engineering and knowledge engineering ;$vv. 20 300 $aDescription based upon print version of record. 311 $a981-4340-91-X 320 $aIncludes bibliographic references. 327 $aPreface; 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 327 $a4.2. 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 327 $a3. 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 327 $a3. 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 327 $a4.1. 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 327 $a5. Rule Extraction to Understand Changes in an Adaptive System Marjorie A. Darrah and Brian J. Taylor 330 $aThis book focuses on the topic of improving software quality using adaptive control approaches. As software systems grow in complexity, some of the central challenges include their ability to self-manage and adapt at run time, responding to changing user needs and environments, faults, and vulnerabilities. Control theory approaches presented in the book provide some of the answers to these challenges. The book weaves together diverse research topics (such as requirements engineering, software development processes, pervasive and autonomic computing, service-oriented architectures, on-line adaptation of software behavior, testing and QoS control) into a coherent whole. Written by world-renowned experts, this book is truly a noteworthy and authoritative reference for students, researchers and practitioners to better understand how the adaptive control approach can be applied to improve the quality of software systems. Book chapters also outline future theoretical and experimental challenges for researchers in this area. -- back cover. 410 0$aSeries on software engineering and knowledge engineering ;$vv. 20. 606 $aSoftware engineering 606 $aComputer software$xDevelopment 608 $aElectronic books. 615 0$aSoftware engineering. 615 0$aComputer software$xDevelopment. 676 $a005.14 701 $aWong$b W. Eric$0908929 701 $aCukic$b Bojan$0908930 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910457436603321 996 $aAdaptive control approach for software quality improvement$92032868 997 $aUNINA