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Combinatorial Testing in Cloud Computing 1st ed. 2017 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 128 pages, height x width: 235x155 mm, weight: 2234 g, 37 Illustrations, color; 18 Illustrations, black and white; X, 128 p. 55 illus., 37 illus. in color., 1 Paperback / softback
  • Sērija : SpringerBriefs in Computer Science
  • Izdošanas datums: 03-Nov-2017
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811044805
  • ISBN-13: 9789811044809
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  • Mīkstie vāki
  • Cena: 46,91 €*
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Combinatorial Testing in Cloud Computing 1st ed. 2017Palielināt
  • Formāts: Paperback / softback, 128 pages, height x width: 235x155 mm, weight: 2234 g, 37 Illustrations, color; 18 Illustrations, black and white; X, 128 p. 55 illus., 37 illus. in color., 1 Paperback / softback
  • Sērija : SpringerBriefs in Computer Science
  • Izdošanas datums: 03-Nov-2017
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811044805
  • ISBN-13: 9789811044809
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789811044809.html
Keywords:
This book introduces readers to an advanced combinatorial testing approach and its application in the cloud environment. Based on test algebra and fault location analysis, the proposed combinatorial testing method can support experiments with 250 components (with 2 * (250) combinations), and can detect the fault location based on the testing results. This function can efficiently decrease the size of candidate testing sets and therefore increase testing efficiency. The proposed solution’s effectiveness in the cloud environment is demonstrated using a range of experiments.
1 Introduction
1(14)
1.1 Software Testing
1(1)
1.2 Cloud Testing
2(1)
1.3 Combinatorial Designs
3(2)
1.3.1 Latin Square
3(1)
1.3.2 Orthogonal Array
3(1)
1.3.3 Covering Array
4(1)
1.4 Combinatorial Testing
5(5)
1.4.1 Covering Array for Testing
6(1)
1.4.2 Automatic Efficient Test Generator
6(2)
1.4.3 In-Parameter-Order
8(1)
1.4.4 Genetic Algorithm
8(1)
1.4.5 Backtracking Algorithm
9(1)
1.4.6 Fault Detection
9(1)
1.5 Structure of This Book
10(5)
References
11(4)
2 Combinatorial Testing in Cloud Computing
15(10)
2.1 Combinatorial Testing in Cloud Computing
15(1)
2.2 Improvements of Combinatorial Testing in Cloud Environment
16(1)
2.3 Faulty Location Analysis in Combinatorial Testing
17(5)
2.3.1 Fault Localization Based on Failure-Inducing Combinations
17(1)
2.3.2 Identifying Failure-Inducing Combinations in a Combinatorial Test Set
18(1)
2.3.3 Faulty Interaction Identification via Constraint Solving and Optimization
19(1)
2.3.4 Characterizing Failure-Causing Parameter Interactions by Adaptive Testing
20(2)
2.3.5 Comparisons of Existing Faulty Location Analysis Solutions
22(1)
2.4 Related Work
22(3)
References
23(2)
3 Adaptive Fault Detection In Multi-tenancy Saas Systems
25(12)
3.1 Adaptive Testing Framework
25(4)
3.1.1 Learning from Earlier Test Results
25(3)
3.1.2 AR Algorithm Framework
28(1)
3.1.3 Relationship Between Faults, Final-Faults, and Candidate-Faults
29(1)
3.2 Simulation of AR Algorithm
29(3)
3.3 Incremental Testing to Allow New Components
32(5)
References
36(1)
4 Test Algebra for Concurrent Combinatorial Testing
37(16)
4.1 Test Algebra
37(9)
4.1.1 Learning from Previous Test Results
38(2)
4.1.2 Changing Test Result Status
40(1)
4.1.3 Matrix Representation
40(2)
4.1.4 Relationship Between Configuration and Its Interactions
42(1)
4.1.5 Merging Concurrent Testing Results
43(2)
4.1.6 Distributive Rule
45(1)
4.1.7 Incremental Development
45(1)
4.2 Conclusion
46(7)
Appendix
46(6)
Reference
52(1)
5 Concurrent Test Algebra Execution with Combinatorial Testing
53(16)
5.1 TA Analysis Framework
53(6)
5.1.1 The Role of N in Concurrent Combinatorial Testing
58(1)
5.1.2 Modified Testing Process
58(1)
5.2 TA Analysis Algorithm
59(1)
5.3 TA Analysis Process and Related Considerations
60(2)
5.3.1 Analysis Process
60(1)
5.3.2 Adjustment in Analyzing
61(1)
5.4 Test Database Design
62(1)
5.4.1 X and F Table Design
62(1)
5.4.2 P Table Design
62(1)
5.4.3 N and U Table Design
63(1)
5.5 Experiment
63(4)
5.6 Conclusion
67(2)
References
67(2)
6 Test Algebra Execution in a Cloud Environment
69(14)
6.1 TA Concurrent Execution and Analysis
69(7)
6.1.1 TA Concurrent Execution
69(2)
6.1.2 NU Configuration
71(1)
6.1.3 NU Configuration Selection Algorithms
72(2)
6.1.4 Analysis Process of NU and U Configurations
74(2)
6.2 TA Experiments
76(5)
6.2.1 TA MapReduce Experiment Flowchart
76(1)
6.2.2 Different Configuration Numbers of TA Experiments
76(1)
6.2.3 Different Speedup Strategy for TA Experiments
76(1)
6.2.4 Different Fault Rates for TA Experiments
77(2)
6.2.5 Explanation on Simulated Data
79(1)
6.2.6 Simulation with Different Clusters
79(1)
6.2.7 Simulation using 37-node Cluster with Different Map Slots
79(2)
6.3 Conclusion
81(2)
Reference
82(1)
7 Adaptive Reasoning Algorithm with Automated Test Cases Generation and Test Algebra in Saas System
83(18)
7.1 Experimentation Using a MTA SaaS Sample
83(3)
7.2 SaaS Testing
86(2)
7.3 SaaS Test Case Generation
88(4)
7.4 Simulation and Analysis
92(9)
7.4.1 Simulation of ARP Algorithm
92(2)
7.4.2 Incremental Testing with Automatic Test Generation
94(1)
7.4.3 Simulation Experiments of ARP+PTR
95(1)
7.4.4 Analysis of the Strategy on Test Generation
96(3)
7.4.5 TA Simulation in SaaS
99(1)
References
99(2)
8 TaaS Design for Combinatorial Testing
101(14)
8.1 TaaS Introduction
101(1)
8.2 TaaS Design with TA and AR
102(3)
8.3 TaaS as SaaS
105(5)
8.3.1 GUIs
107(1)
8.3.2 Workflows
107(1)
8.3.3 Services
108(1)
8.3.4 Runtime Composition, Execution and Scalability
108(2)
8.4 Experimental Results
110(2)
8.5 Conclusion
112(3)
References
112(3)
9 Integrated Taas with Fault Detection and Test Algebra
115
9.1 Framework
115(4)
9.1.1 Integrated Process
115(1)
9.1.2 Framework Illustration
116(3)
9.2 Experiments and Results
119(8)
9.2.1 Experiment Setup
119(3)
9.2.2 Experiment Results
122(1)
9.2.3 Measurements
123(4)
9.3 Conclusion
127
References
127
Wei-Tek Tsai is currently a professor at both the School of Computing Informatics and Decision Systems Engineering at Arizona State University, USA and the School of Computer Science and Engineering at Beihang University, China. He received his PhD and MS in Computer Science from the University of California at Berkeley, and his BS in Computer Science and engineering from MIT. He has produced over 400 papers in various journals and conferences, received two Best Paper awards, and awarded several Guest Professorships. His work has been supported by the US Department of Defense, Department of Education, National Science Foundation, the EU, and industrial companies such as Intel, Fujitsu and Guidant. In the last ten years, he has focused his energies on service-oriented computing and SaaS, as well as various aspects of software engineering including blockchain, architecture, testing, and maintenance. Guanqiu Qi received his PhD in Computer Science from Arizona State University in 2014. His research interests span many aspects of software engineering, such as SaaS (Software-as-a-Service), TaaS (Testing-as-a-Service), Big Data Testing, Combinatorial Testing, and Service-Oriented Computing, as well as blockchain.