Foreword xv
Preface xix
Acknowledgments xxiii
List of Contributors xxv
Part I The Software Landscape 1
1 Software Crisis 2.0 3
Brian Fitzgerald
1.1 Software Crisis 1.0 3
1.2 Software Crisis 2.0 5
1.2.1 Hardware Advances 6
1.2.2 “Big Data” 8
1.2.3 Digital Natives Lifelogging and the Quanti?ed Self 9
1.2.4 Software-De?ned∗ 10
1.3 Software Crisis 2.0: The Bottleneck 10
1.3.1 Signi?cant Increase in Volume of Software Required 11
1.3.2 New Skill Sets Required for Software Developers 12
1.4 Conclusion 13
References 14
2 Simplicity as a Driver for Agile Innovation 17
Tiziana Margaria and Bernhard Steffen
2.1 Motivation and Background 17
2.2 Important Factors 20
2.3 The Future 22
2.4 Less Is More: The 80/20 Principle 27
2.5 Simplicity: A Never Ending Challenge 28
2.6 IT Speci?cs 29
2.7 Conclusions 29
Acknowledgments 30
References 30
3 Intercomponent Dependency Issues in Software Ecosystems 35
Maëlick Claes, Alexandre Decan, and Tom Mens
3.1 Introduction 35
3.2 Problem Overview 36
3.2.1 Terminology 36
3.2.2 Identifying and Retrieving Dependency Information 38
3.2.3 Satisfying Dependencies and Con?icts 39
3.2.4 Component Upgrade 40
3.2.5 Inter-Project Cloning 41
3.3 First Case Study: Debian 42
3.3.1 Overview of Debian 42
3.3.2 Aggregate Analysis of Strong Con?icts 44
3.3.3 Package-Level Analysis of Strong Con?icts 45
3.4 Second Case Study: The R Ecosystem 46
3.4.1 Overview of R 46
3.4.2 R Package Repositories 47
3.4.3 Interrepository Dependencies 50
3.4.4 Intrarepository Dependencies 52
3.5 Conclusion 53
Acknowledgments 54
References 54
4 Triangulating Research Dissemination Methods: A Three-Pronged Approach to Closing the Research–Practice Divide 58
Sarah Beecham, Ita Richardson, Ian Sommerville, Padraig O’Leary, Sean Baker, and John Noll
4.1 Introduction 58
4.2 Meeting the Needs of Industry 60
4.2.1 Commercialization Feasibility Study 61
4.2.2 Typical GSE Issues Were Reported 62
4.3 The Theory–Practice Divide 63
4.3.1 Making Research Accessible 64
4.3.2 Where Do Practitioners Really Go for Support? 65
4.4 Solutions: Rethinking Our Dissemination Methods 66
4.4.1 Workshops, Outreach, and Seminars 66
4.4.2 Case Studies 69
4.4.3 Action Research 70
4.4.4 Practitioner Ph.D.’s 71
4.4.5 Industry Fellowships 73
4.4.6 Commercializing Research 74
4.5 Obstacles to Research Relevance 76
4.5.1 The (IR) Relevance of Academic Software Engineering Research 76
4.5.2 Barriers to Research Commercialization 77
4.5.3 Academic Barriers to Commercialization 78
4.5.4 Business Barriers to Commercialization 79
4.5.5 Organizational Barriers to Commercialization 80
4.5.6 Funding Barriers to Commercialization 81
4.6 Conclusion 84
4.6.1 Research and Practice Working Together to Innovate 85
4.6.2 Final Thoughts 86
Acknowledgments 86
References 86
Part II Autonomous Software Systems 91
5 Apoptotic Computing: Programmed Death by Default for Software Technologies 93
Roy Sterritt and Mike Hinchey
5.1 Biological Apoptosis 93
5.2 Autonomic Agents 94
5.3 Apoptosis within Autonomic Agents 96
5.4 NASA SWARM Concept Missions 98
5.5 The Evolving State-of-the-Art Apoptotic Computing 100
5.5.1 Strong versus Weak Apoptotic Computing 100
5.5.2 Other Research 101
5.6 “This Message Will Self-Destruct”: Commercial Applications 102
5.7 Conclusion 102
Acknowledgments 103
References 103
6 Requirements Engineering for Adaptive and Self-Adaptive Systems 107
Emil Vassev and Mike Hinchey
6.1 Introduction 107
6.2 Understanding ARE 108
6.3 System Goals and Goals Models 109
6.4 Self-∗ Objectives and Autonomy-Assistive Requirements 111
6.4.1 Constraints and Self-∗ Objectives 113
6.4.2 Mission Analysis and Self-∗ Objectives 114
6.5 Recording and Formalizing Autonomy Requirements 116
6.5.1 ARE Requirements Chunk 117
6.6 Conclusion 118
Acknowledgments 119
References 119
7 Toward Arti?cial Intelligence through Knowledge Representation for Awareness 121
Emil Vassev and Mike Hinchey
7.1 Introduction 121
7.2 Knowledge Representation 122
7.2.1 Rules 122
7.2.2 Frames 122
7.2.3 Semantic Networks and Concept Maps 122
7.2.4 Ontologies 123
7.2.5 Logic 123
7.2.6 Completeness and Consistency 124
7.2.7 Reasoning 125
7.2.8 Technologies 125
7.3 KnowLang 126
7.3.1 Modeling Knowledge with KnowLang 127
7.3.2 Knowledge Representation for Self-Adaptive Behavior 129
7.3.3 Case Study 129
7.4 Awareness 131
7.4.1 Classes of Awareness 132
7.4.2 Structuring Awareness 133
7.4.3 Implementing Awareness 134
7.5 Challenges and Conclusion 136
References 136
Part III Software Development and Evolution 139
8 Continuous Model-Driven Engineering 141
Tiziana Margaria, Anna-Lena Lamprecht, and Bernhard Steffen
8.1 Introduction 141
8.2 Continuous Model-Driven Engineering 143
8.3 CMDE in Practice 147
8.4 Conclusion 150
Acknowledgment 150
References 151
9 Rethinking Functional Requirements: A Novel Approach Categorizing System and Software Requirements 155
Manfred Broy
9.1 Introduction 155
9.2 Discussion: Classifying Requirements – Why and How 158
9.2.1 On Classifying Requirements as Being Functional 158
9.2.2 “Nonfunctional” Requirements and Their Characterization 159
9.2.3 Limitations of Classi?cation Due to Heterogeneity and Lacking Precision 160
9.2.4 Approach: System Model-Based Categorization of Requirements 162
9.3 The System Model 164
9.3.1 The Basics: System Modeling Ontology 164
9.3.2 System Views and Levels of Abstractions 171
9.3.3 Structuring Systems into Views 172
9.4 Categorizing System Properties 172
9.4.1 System Behavior: Behavioral Properties 173
9.4.2 Variations in Modeling System Behavior 175
9.4.3 System Context: Properties of the Context 176
9.4.4 Nonbehavioral System Properties: System Representation 177
9.5 Categorizing Requirements 178
9.5.1 A Rough Categorization of Requirements 179
9.5.2 A Novel Taxonomy of Requirements? 183
9.6 Summary 186
Acknowledgments 187
References 187
10 The Power of Ten—Rules for Developing Safety Critical Code 188
Gerard J. Holzmann
10.1 Introduction 188
10.2 Context 189
10.3 The Choice of Rules 190
10.4 Ten Rules for Safety Critical Code 192
10.5 Synopsis 200
References 201
11 Seven Principles of Software Testing 202
Bertrand Meyer
11.1 Introduction 202
11.2 De?ning Testing 202
11.3 Tests and Speci?cations 203
11.4 Regression Testing 204
11.5 Oracles 204
11.6 Manual and Automatic Test Cases 205
11.7 Testing Strategies 205
11.8 Assessment Criteria 206
11.9 Conclusion 207
References 207
12 Analyzing the Evolution of Database Usage in Data-Intensive Software Systems 208
Loup Meurice, Mathieu Goeminne, Tom Mens, Csaba Nagy, Alexandre Decan, and Anthony Cleve
12.1 Introduction 208
12.2 State of the Art 210
12.2.1 Our Own Research 211
12.3 Analyzing the Usage of ORM Technologies in Database-Driven Java Systems 212
12.4 Coarse-Grained Analysis of Database Technology Usage 215
12.4.5 Discussion 222
12.5 Fine-Grained Analysis of Database Technology Usage 222
12.5.1 Analysis Background 222
12.5.2 Conceptual Schema 224
12.5.3 Metrics 226
12.5.4 Discussion 235
12.6 Conclusion 236
12.7 Future Work 237
Acknowledgments 238
References 238
Part IV Software Product Lines and Variability 41
13 Dynamic Software Product Lines 243
Svein Hallsteinsen, Mike Hinchey, Sooyong Park, and Klaus Schmid
13.1 Introduction 243
13.2 Product Line Engineering 243
13.3 Software Product Lines 244
13.4 Dynamic SPLs 245
References 246
14 Cutting-Edge Topics on Dynamic Software Variability 247
Rafael Capilla, Jan Bosch, and Mike Hinchey
14.1 Introduction 247
14.2 The Postdeployment Era 248
14.3 Runtime Variability Challenges Revisited 249
14.4 What Industry Needs from Variability at Any Time? 253
14.5 Approaches and Techniques for Dynamic Variability Adoption 255
14.6 Summary 266
14.7 Conclusions 267
References 268
Part V Formal Methods 271
15 The Quest for Formal Methods in Software Product Line Engineering 273
Reiner Hahnle and Ina Schaefer
15.1 Introduction 273
15.2 SPLE: Bene?ts and Limitations 274
15.3 Applying Formal Methods to SPLE 275
15.4 The Abstract Behavioral Speci?cation Language 277
15.5 Model-Centric SPL Development with ABS 279
15.6 Remaining Challenges 280
15.6.4 Maintenance 280
15.7 Conclusion 281
References 281
16 Formality, Agility, Security, and Evolution in Software Engineering 282
Jonathan P. Bowen, Mike Hinchey, Helge Janicke, Martin Ward, and Hussein Zedan
16.1 Introduction 282
16.2 Formality 283
16.3 Agility 283
16.4 Security 284
16.5 Evolution 285
16.6 Conclusion 289
Acknowledgments 290
References 290
Part VI Cloud Computing 293
17 Cloud Computing: An Exploration of Factors Impacting Adoption 295
Lorraine Morgan and Kieran Conboy
17.1 Introduction 295
17.2 Theoretical Background 296
17.3 Research Method 298
17.4 Findings and Analysis 303
17.4.2 Organizational Factors Impacting Adoption 306
17.4.3 Environmental Factors Impacting Adoption 308
17.5 Discussion and Conclusion 310
17.5.1 Limitations and Future Research 311
References 311
18 A Model-Centric Approach to the Design of Resource-Aware Cloud Applications 315
Reiner Hahnle and Einar Broch Johnsen
18.1 Capitalizing on the Cloud 315
18.2 Challenges 316
18.2.1 Empowering the Designer 316
18.2.2 Deployment Aspects at Design Time 316
18.3 Controlling Deployment in the Design Phase 318
18.4 ABS: Modeling Support for Designing Resource-Aware Applications 319
18.5 Resource Modeling with ABS 320
18.6 Opportunities 324
18.6.1 Fine-Grained Provisioning 324
18.6.2 Tighter Provisioning 324
18.6.3 Application-Speci?c Resource Control 324
18.6.4 Application-Controlled Elasticity 324
18.7 Summary 325
Acknowledgments 325
References 325
Index 327
