Advances in computers Volume one hundred and five Volume one hundred and five /
Advances in Computers, the latest volume in the series published since 1960, presents detailed coverage of innovations in computer hardware, software, theory, design, and applications. In addition, it provides contributors with a medium in which they can explore their subjects in greater depth and b...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Massachusetts :
Academic Press
2017.
|
| Edición: | First edition |
| Colección: | Advances in Computers,
Volume 105 |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630294906719 |
Tabla de Contenidos:
- Front Cover
- Advances in Computers
- Copyright
- Contents
- Preface
- Chapter One: Web-Based Behavioral Modeling for Continuous User Authentication (CUA)
- 1. Introduction
- 1.1. Continuous User Authentication Scenarios
- 1.2. Approach
- 1.3. Scope of Work
- 1.4. Broader Impact
- 1.5. Chapter Outline
- 2. Background
- 2.1. Classical Authentication Methods
- 2.2. Defining Continuous User Authentication
- 2.3. Existing Continuous User Authentication Techniques
- 2.4. Statistical Language Modeling
- 2.4.1. Neural Networks
- 2.4.2. Maximum Entropy
- 2.4.3. Probabilistic Context-Free Grammars
- 2.4.4. Decision Trees
- 2.4.5. n-grams
- 2.4.6. Hidden Markov Models
- 3. Intruder Detector: A Tool for CUA
- 3.1. Web-Based User Behavior Analysis
- 3.2. Feasibility Study
- 3.3. CUA Paradigm for Web-Based Applications
- 3.3.1. Contribution 1: Model Selection
- 3.3.2. Contribution 2: Keyword Abstraction
- 3.3.3. Contribution 3: Tool Support
- 3.3.4. Contribution 4: Evaluation Criteria
- 3.4. Continuous User Authentication Infrastructure
- 3.5. Intruder Detector in Action
- 4. Conclusions
- 4.1. Future Work
- References
- Chapter Two: Advances in Model-Based Testing of GUI-Based Software
- 1. Introduction
- 1.1. GUI Testing Challenges
- 1.2. Radio Button Demo
- 2. Methods
- 2.1. Standard Capture/Replay
- 2.2. Capture/Replay Using Image Recognition
- 2.3. Finite State Machine
- 2.4. Variable Finite State Machine
- 2.5. Complete Interaction Sequence
- 2.6. Off-Nominal Finite State Machine
- 2.7. Event-Flow Graph
- 2.8. Event-Interaction Graph
- 2.9. Event-Semantic Interaction Graph
- 2.10. Planning
- 2.11. Genetic Algorithm
- 2.12. Covering Arrays
- 2.13. Summary
- 3. Interacting GUI Events
- 3.1. Event-Code Interaction
- 3.2. Composite Event-Code Interaction
- 4. Comparing GUI Models
- 5. Conclusion.
- References
- Chapter Three: Fault Localization Using Hybrid Static/Dynamic Analysis
- 1. Introduction
- 2. Motivating Example
- 3. Existing Approaches
- 3.1. Slicing
- 3.2. Differential Techniques
- 3.2.1. Techniques Based on Working and Nonworking Program Versions
- 3.2.2. Techniques Based on Passed and Failed Test Cases
- 3.3. Techniques Based on Failed Test Cases
- 3.4. Machine Learning-Based Approaches
- 3.5. Model-Based Approaches
- 3.6. Performance Debugging
- 4. Modeling Disqover
- 4.1. Motivating Example
- 4.2. The Disqover Approach
- 4.2.1. The Execution Trace &
- Logs Extraction
- 4.2.2. Test Cases Partitioning
- 4.3. Common Subsequences Extraction
- 4.3.1. Applying Code Coverage Intersection
- 4.3.2. Constructing the Common Subsequences Graph
- 4.3.3. Extracting Common Subsequences
- 4.3.4. Algorithm Optimizations
- 4.3.5. Test Case Abstraction
- 4.3.5.1. Loop-Based Abstraction
- 4.3.5.2. Block-Based Abstraction
- 4.3.6. Extracting the Most Important Subsequences
- 4.3.7. Hybrid Dynamic/Static Analysis
- 4.3.8. Remote Debugging
- 5. Conclusion
- References
- Chapter Four: Characterizing Software Test Case Behavior With Regression Models
- 1. Motivation
- 1.1. Finding Bugs in Software
- 1.2. Software Quality Assurance Activities
- 1.3. Software Testing Activities
- 1.4. Testing EDS
- 1.5. Automated GUI Testing With Models
- 1.5.1. GUI Testing
- 1.5.2. Existing Model-Based Approaches Which Incorporate Context
- 1.6. Problems With Existing MBT Approaches
- 2. Background
- 2.1. Model-Based Testing
- 2.1.1. State-Based and Data-Flow Models
- 2.1.2. Event-Flow Models
- 2.2. GUI Testing
- 2.2.1. Test Oracles
- 2.3. Summary
- 3. A Workflow for Predictive Modeling
- 3.1. The GUITAR Standard Workflow
- 3.1.1. A Note About Application Platform
- 3.1.2. The GUI Ripper
- 3.1.3. Ripper Algorithm.
- 3.1.4. Configuration Options and Customizations
- 3.1.5. Event Properties
- 3.1.6. Conversion
- 3.1.7. Test Case Generation
- 3.1.8. Test Case Replay
- 3.2. Adding Predictive Regression Models
- 3.2.1. MBT Problems That Predictive Regression Models Can Address
- 3.2.2. Supervised Learning and Binary Classification
- 3.2.3. Generalized Linear Model
- 3.2.4. Adding Regularization With Lasso
- 3.2.5. Modeling Summary
- 4. A Scalable Framework for Execution
- 4.1. Overview
- 4.2. Portable Configurations With Docker
- 4.3. Docker Image Details
- 4.3.1. Job Execution With Jenkins CI
- 4.3.2. Test and Experiment Artifact Persistence With MongoDB
- 4.3.3. Code Library Artifact Persistence With Maven and Sonatype Nexus
- 4.3.4. GUITAR Slave
- 4.3.5. R Slave
- 4.4. Persistence With TestData and MongoDB
- 4.5. Execution of Automated Jobs in Parallel With Jenkins CI
- 4.6. Framework Summary
- 5. Recent Research: A Predictive Regression Model for Test Case Feasibility
- 5.1. Defining Feasibility
- 5.2. The Impact of Infeasibility
- 5.3. Related Work on Test Case Infeasibility
- 5.4. Modeling Infeasibility
- 5.4.1. Applying Labels From Replayer Output
- 5.4.2. Feature Selection
- 5.5. Working With Models in R
- 5.5.1. Preparing Data
- 5.5.2. Selecting and Using a Model
- 5.6. Summary of Experimental Results
- 6. Concluding Remarks
- 6.1. Summary
- 6.2. Future Directions
- References
- Contents of Volumes in this Series
- Back Cover.
