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The Resource Quality software through reuse and integration

Quality software through reuse and integration

Work
Publication
Extent
1 online resource (323 pages).
Note
3.2 Classification
Contents
  • Preface; Contents; On the Tractable Acquisition of Heuristics for Software Synthesis Demonstrating that PsimNP; Abstract; 1 Introduction; 2 The Heuristic TSP Algorithm; 3 TSP Results; 3.1 Graphic Presentation; 3.2 Five-City Tour; 3.3 Twenty-Two-City Tour; 4 A Synthetic Intelligence for Secure Software Construction; 4.1 Code Automation for Information Dominance; 4.2 Auto-Randomized Learning for the TSP; 4.3 Auto-Randomization Through Property Lists; 4.4 State and Representation Randomization; 4.5 On Self-randomization for Generalization; 5 Conclusion; Acknowledgements; References
  • An Approach Transmutation-Based in Case-Based ReasoningAbstract; 1 Introduction; 2 Related Work; 3 Transmutation Approach; 3.1 Problem Substitution; 3.2 Solution Substitution; 4 Knowledge Induction in a CBR System; 4.1 Knowledge Structuration; 4.2 Search for Accurate Knowledge; 5 Illustration of the Transmutation Approach; 5.1 Domain Analysis; 5.2 Domain Semantics; 5.3 Transmutation Based on Problem Substitution; 5.4 Transmutation Based on Solution Substitution; 6 Validation of the Randomized Knowledge; 6.1 Validation Rules; 6.2 Application to Strong Sequencing
  • 6.3 Application to Weak Sequencing6.4 Application to Free Sequencing; 7 Transmutation in Case Based-Reasoning; 7.1 Problem Transmutation in CBR; 7.2 Solution Transmutation in CBR; 8 Implementation; 9 Conclusion; References; Utilizing Semantic Techniques for Automatic Code Reuse in Software Repositories; 1 Introduction; 2 Related Work; 2.1 Example Code Recommendation; 2.2 Component Retrieval; 3 Ontology-Based Software Representation; 3.1 Knowledge Base Creation; 4 Component Retrieval; 4.1 Component Search Scenario; 5 Automatic Code Recommendation; 5.1 Code Recommendation Scenario
  • 5.2 Dealing with Downcasts6 Evaluation; 6.1 Component and Code Search Experiment; 6.2 Human Experiment; 6.3 Threats to Validity; 7 Conclusion; References; A Multi-strategy Approach for Ontology Reuse Through Matching and Integration Techniques; 1 Introduction; 1.1 Ontology Matching and Integration Background; 2 Related Works; 2.1 Ontology Reuse; 3 The Proposed Framework for Ontology Integration and Reuse; 3.1 Reference Models Retrieval; 3.2 Reference Model Reconciliation and Normalization; 3.3 Reference Model Matching; 3.4 Reference Models Merging or Integration; 4 The Matching Methodology
  • 4.1 The String-Based Matching4.2 The Linguistic Matching; 4.3 Extended Linguistic Matching; 5 A Case Study from the Food Domain; 6 Experimental Results; 7 Conclusions; A Appendix; References; Classifier Fusion by Judgers on Spark Clusters for Multimedia Big Data Classification; Abstract; 1 Introduction; 2 Related Work; 2.1 Multiple Classifiers and Features; 2.2 Multi-classifier Fusion; 2.2.1 Weighted Combination Strategy; 2.2.2 Statistics-Based Strategy; 2.2.3 Regression-Based Strategy; 2.2.4 Bayesian Probabilistic Strategy; 3 The Proposed Framework; 3.1 Feature Extraction
Isbn
9783319561578
Instance
Label
Quality software through reuse and integration
Title
Quality software through reuse and integration
Contributor
Subject
Language
eng
Member of
Cataloging source
EBLCP
Dewey number
  • 005
  • 620
Index
no index present
LC call number
  • QA76.76.R47
  • TA1-2040
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Rubin, S.
  • Bouabana-Tebibel, Thouraya
Series statement
Advances in Intelligent Systems and Computing
Series volume
v. 561
http://library.link/vocab/subjectName
  • Computer software
  • Computer software
Label
Quality software through reuse and integration
Instantiates
Publication
Note
3.2 Classification
Antecedent source
unknown
Bibliography note
Includes bibliographical references and author index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Preface; Contents; On the Tractable Acquisition of Heuristics for Software Synthesis Demonstrating that PsimNP; Abstract; 1 Introduction; 2 The Heuristic TSP Algorithm; 3 TSP Results; 3.1 Graphic Presentation; 3.2 Five-City Tour; 3.3 Twenty-Two-City Tour; 4 A Synthetic Intelligence for Secure Software Construction; 4.1 Code Automation for Information Dominance; 4.2 Auto-Randomized Learning for the TSP; 4.3 Auto-Randomization Through Property Lists; 4.4 State and Representation Randomization; 4.5 On Self-randomization for Generalization; 5 Conclusion; Acknowledgements; References
  • An Approach Transmutation-Based in Case-Based ReasoningAbstract; 1 Introduction; 2 Related Work; 3 Transmutation Approach; 3.1 Problem Substitution; 3.2 Solution Substitution; 4 Knowledge Induction in a CBR System; 4.1 Knowledge Structuration; 4.2 Search for Accurate Knowledge; 5 Illustration of the Transmutation Approach; 5.1 Domain Analysis; 5.2 Domain Semantics; 5.3 Transmutation Based on Problem Substitution; 5.4 Transmutation Based on Solution Substitution; 6 Validation of the Randomized Knowledge; 6.1 Validation Rules; 6.2 Application to Strong Sequencing
  • 6.3 Application to Weak Sequencing6.4 Application to Free Sequencing; 7 Transmutation in Case Based-Reasoning; 7.1 Problem Transmutation in CBR; 7.2 Solution Transmutation in CBR; 8 Implementation; 9 Conclusion; References; Utilizing Semantic Techniques for Automatic Code Reuse in Software Repositories; 1 Introduction; 2 Related Work; 2.1 Example Code Recommendation; 2.2 Component Retrieval; 3 Ontology-Based Software Representation; 3.1 Knowledge Base Creation; 4 Component Retrieval; 4.1 Component Search Scenario; 5 Automatic Code Recommendation; 5.1 Code Recommendation Scenario
  • 5.2 Dealing with Downcasts6 Evaluation; 6.1 Component and Code Search Experiment; 6.2 Human Experiment; 6.3 Threats to Validity; 7 Conclusion; References; A Multi-strategy Approach for Ontology Reuse Through Matching and Integration Techniques; 1 Introduction; 1.1 Ontology Matching and Integration Background; 2 Related Works; 2.1 Ontology Reuse; 3 The Proposed Framework for Ontology Integration and Reuse; 3.1 Reference Models Retrieval; 3.2 Reference Model Reconciliation and Normalization; 3.3 Reference Model Matching; 3.4 Reference Models Merging or Integration; 4 The Matching Methodology
  • 4.1 The String-Based Matching4.2 The Linguistic Matching; 4.3 Extended Linguistic Matching; 5 A Case Study from the Food Domain; 6 Experimental Results; 7 Conclusions; A Appendix; References; Classifier Fusion by Judgers on Spark Clusters for Multimedia Big Data Classification; Abstract; 1 Introduction; 2 Related Work; 2.1 Multiple Classifiers and Features; 2.2 Multi-classifier Fusion; 2.2.1 Weighted Combination Strategy; 2.2.2 Statistics-Based Strategy; 2.2.3 Regression-Based Strategy; 2.2.4 Bayesian Probabilistic Strategy; 3 The Proposed Framework; 3.1 Feature Extraction
Control code
SPR1001371093
Dimensions
unknown
Extent
1 online resource (323 pages).
File format
unknown
Form of item
online
Isbn
9783319561578
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1001371093
  • (OCoLC)1001371093
Label
Quality software through reuse and integration
Publication
Note
3.2 Classification
Antecedent source
unknown
Bibliography note
Includes bibliographical references and author index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Preface; Contents; On the Tractable Acquisition of Heuristics for Software Synthesis Demonstrating that PsimNP; Abstract; 1 Introduction; 2 The Heuristic TSP Algorithm; 3 TSP Results; 3.1 Graphic Presentation; 3.2 Five-City Tour; 3.3 Twenty-Two-City Tour; 4 A Synthetic Intelligence for Secure Software Construction; 4.1 Code Automation for Information Dominance; 4.2 Auto-Randomized Learning for the TSP; 4.3 Auto-Randomization Through Property Lists; 4.4 State and Representation Randomization; 4.5 On Self-randomization for Generalization; 5 Conclusion; Acknowledgements; References
  • An Approach Transmutation-Based in Case-Based ReasoningAbstract; 1 Introduction; 2 Related Work; 3 Transmutation Approach; 3.1 Problem Substitution; 3.2 Solution Substitution; 4 Knowledge Induction in a CBR System; 4.1 Knowledge Structuration; 4.2 Search for Accurate Knowledge; 5 Illustration of the Transmutation Approach; 5.1 Domain Analysis; 5.2 Domain Semantics; 5.3 Transmutation Based on Problem Substitution; 5.4 Transmutation Based on Solution Substitution; 6 Validation of the Randomized Knowledge; 6.1 Validation Rules; 6.2 Application to Strong Sequencing
  • 6.3 Application to Weak Sequencing6.4 Application to Free Sequencing; 7 Transmutation in Case Based-Reasoning; 7.1 Problem Transmutation in CBR; 7.2 Solution Transmutation in CBR; 8 Implementation; 9 Conclusion; References; Utilizing Semantic Techniques for Automatic Code Reuse in Software Repositories; 1 Introduction; 2 Related Work; 2.1 Example Code Recommendation; 2.2 Component Retrieval; 3 Ontology-Based Software Representation; 3.1 Knowledge Base Creation; 4 Component Retrieval; 4.1 Component Search Scenario; 5 Automatic Code Recommendation; 5.1 Code Recommendation Scenario
  • 5.2 Dealing with Downcasts6 Evaluation; 6.1 Component and Code Search Experiment; 6.2 Human Experiment; 6.3 Threats to Validity; 7 Conclusion; References; A Multi-strategy Approach for Ontology Reuse Through Matching and Integration Techniques; 1 Introduction; 1.1 Ontology Matching and Integration Background; 2 Related Works; 2.1 Ontology Reuse; 3 The Proposed Framework for Ontology Integration and Reuse; 3.1 Reference Models Retrieval; 3.2 Reference Model Reconciliation and Normalization; 3.3 Reference Model Matching; 3.4 Reference Models Merging or Integration; 4 The Matching Methodology
  • 4.1 The String-Based Matching4.2 The Linguistic Matching; 4.3 Extended Linguistic Matching; 5 A Case Study from the Food Domain; 6 Experimental Results; 7 Conclusions; A Appendix; References; Classifier Fusion by Judgers on Spark Clusters for Multimedia Big Data Classification; Abstract; 1 Introduction; 2 Related Work; 2.1 Multiple Classifiers and Features; 2.2 Multi-classifier Fusion; 2.2.1 Weighted Combination Strategy; 2.2.2 Statistics-Based Strategy; 2.2.3 Regression-Based Strategy; 2.2.4 Bayesian Probabilistic Strategy; 3 The Proposed Framework; 3.1 Feature Extraction
Control code
SPR1001371093
Dimensions
unknown
Extent
1 online resource (323 pages).
File format
unknown
Form of item
online
Isbn
9783319561578
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1001371093
  • (OCoLC)1001371093

Subject

Member of

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