CONTENTS -- Preface -- 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 Preference -- Indirect 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 Systems -- 3.1. 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 Towey -- 1. 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 Overview -- 4.2. 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 -- 1. Neural Network Rule Extraction -- 1.1. Background on Rule Extraction -- 2. Rule Extraction for System Verification and Validation -- 2.1. An Example of Rule Extraction for the Dynamic Cell Structure Neural Network Used in a System -- 2.1.1. Refining the Algorithm -- 3. Applying Rule Extraction in a Tool for Verification and Validation -- 3.1. Describing a Neural Network with Metadata Expressions -- 3.2. Building a Tool for Rule Extraction -- 3.3. An Example of the Process -- 3.3.1. Translating DCS into NNML -- 3.3.2. Extract Rules -- 3.3.3. Analyze Rules.

4. Verification and Validation Examples -- Scenario 1: Human Understandable Rules Led to Identi.cation of Coding Error. -- Scenario 2: Machine Understandable Rules Led to Identi.cation of Two Coding Errors. -- 5. Potential Applications -- 5.1. Certification of Neural Networks -- 5.2. Health and Status Monitoring of the Neural Network -- 5.3. Extracted Rules as Basis for Expert Systems -- 6. Conclusion -- Acknowledgements -- References -- Appendix A -- 6. Requirements Engineering Via Lyapunov Analysis for Adaptive Flight Control Systems Giampiero Campa, Marco Mammarella, Mario L. Fravolini and Bojan Cukic -- 1. Introduction -- 2. The Framework for Adaptive Augmentation -- 2.1. The Plant, the Closed Loop System and the Error Dynamics -- 2.2. The Linear Controller, the Closed Loop System and the Error Dynamics -- 2.3. The Uncertainty -- 2.4. The Adaptive Element -- 2.5. The Adaptive Augmentation -- 3. The Lyapunov Analysis -- 3.1. Typical "Completion of Squares" Bounds Formulation and its Limitations -- 3.2. A Better Characterization of the Return Set -- 3.3. Boundedness Conditions -- 3.3.1. Extreme Points of the Boundary and Semi-Axes of the Ellipsoid -- 4. Case Study -- 4.0.1. 2D Bounds Calculation and Visualization -- 5. Conclusions -- References -- Periodicals -- Books -- Proceedings -- Computer Software -- 7. Quantitative Modeling for Incremental Software Process Control Scott D. Miller, Raymond A. Decarlo and Aditya P. Mathur -- 1. Introduction -- 1.1. Contributions -- 1.2. Related Work -- 2. General Modeling Strategy -- 2.1. Mathematical Modeling of Productive Capability -- 2.2. State Model of Productive Capability -- 2.3. State Model of a Queue -- 2.4. Normalization of Work Items -- 2.5. Managing Dependencies and Scheduling Constraints within the Model -- 2.6. An Algebraic Model of Activity Coordination.

2.7. Defect Modeling and the Failure Analysis Activity -- 2.8. An Algebraic Model for the Example Defect Population Estimation Component -- 2.9. An Algebraic Model of the Defect Detection Component -- 2.10. An Algebraic Model of the Proportional Splitting Component -- 3. Assembling the Model -- 4. A Simulation Study -- 4.1. Simulation Method -- 4.2. Simulation Results -- 4.2.1. Feature Coding -- 4.2.2. Test Case Coding -- 4.2.3. New Test Case Execution -- 4.2.4. Regression Test Case Execution -- 4.2.5. Defect Introduction, Defect Detection, and Failure Analysis -- 4.2.6. Feature Correction -- 4.2.7. Test Case Correction -- 5. Model Calibration -- 5.1. Calibrating with Ratio Scale Data -- 5.2. Calibrating with Interval Scale Data -- 6. Discussion -- References -- Appendix A: Modeling the Example Process -- Appendix B: Simulation Study Parameters -- 8. Proactive Monitoring and Control of Workflow Execution in Adaptive Service-based Systems Stephen S. Yau and Dazhi Huang -- 1. Introduction -- 2. Current State of the Art -- 3. Background -- 3.1. Workflow Virtual Machine -- 3.2. α-Calculus -- 4. Synthesizing Software Modules for Proactive Monitoring and Control of Workflow Execution in ASBS -- 4.1. Workflow Execution, Monitoring and Control in ASBS -- 4.2. Synthesizing WF Monitors -- 4.3. Synthesizing WF Controllers -- 5. Conclusions and Future Work -- Acknowledgement -- References -- 9. Accelerated Life Tests and Software Aging Rivalino Matias Jr. and Kishor S. Trivedi -- 1. Introduction -- 2. Software Aging Theory -- Classical Software Failure Mechanics -- Fundamentals of Software Aging -- 3. Accelerated Life Tests -- Accelerated Degradation Tests -- 4. Case Study -- Numerical Results -- 5. Final Remarks -- References.