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0 (hodnocen0 x )

EB

ONLINE

Cham : Springer International Publishing AG, 2022

1 online resource (290 pages)

ISBN 9783030831288 (electronic bk.)

ISBN 9783030831271

Print version: Felderer, Michael Ernst Denert Award for Software Engineering 2020 Cham : Springer International Publishing AG,c2022 ISBN 9783030831271

Intro -- Contents -- Ernst Denert Software Engineering Award 2020 -- 1 Introduction -- 2 Overview of the Nominated PhD Theses -- 3 The Work of the Award Winner -- 4 Structure of the Book -- Thanks -- References -- Some Patterns of Convincing Software Engineering Research, or: How to Win the Ernst Denert Software Engineering Award 2020 -- 1 Introduction -- 2 Be in Scope -- 3 Enumerate Your Assumptions -- 4 Delineate Your Contribution -- 5 Honestly Discuss Limitations -- 6 Show Usefulness and Practical Applicability -- 7 Have a Well-Prepared Nutshell -- 8 Be Timeless -- What You See Is What You Get: Practical Effect Handlers in Capability-Passing Style -- 1 Introduction -- 2 Effect Handlers -- 2.1 Aborting the Computation -- 2.2 Dynamic Dependencies -- 2.3 Advanced Control Flow -- 3 Effect Handlers and Object-Oriented Programming -- 3.1 Capability Passing -- 4 Lexically Scoped Effect Handlers: What You See Is What You Get -- 4.1 Dynamically Scoped Effect Handlers -- 4.2 Dynamic vs. Lexical Scoping -- 4.3 Lexically Scoped Effect Handlers -- 4.3.1 Effect Types Carry Meaning -- 4.4 Effect Parametricity -- 4.5 Effect Polymorphism -- 4.5.1 The Traditional Reading -- 4.5.2 The Contextual Reading -- 4.5.3 Parametric vs. Contextual Effect Polymorphism -- 4.5.4 Contextual Effect Polymorphism -- 4.6 What You See Is What You Get -- 5 Improving the Performance of Effect Handlers -- 5.1 Optimizing Handler Search -- 5.1.1 Optimizing Tail Resumptions -- 5.2 Optimizing Continuation Capture -- 5.3 Full Elimination of Control Abstractions -- 5.4 Performance Evaluation -- 6 Related Work -- 7 Conclusion and Future Directions -- 7.1 Future Directions -- References -- How to Effectively Reduce Failure Analysis Time? -- 1 Introduction -- 2 Failure Clustering -- 2.1 Clustering Approach -- 2.1.1 Failure Clustering with Coverage -- 2.1.2 Failure Clustering Without Coverage.

2.2 Industry Impact -- 3 Fault Localization -- 3.1 Syntactic Block Granularity -- 3.2 Re-ranking Program Elements -- 3.3 Evaluation -- 3.4 Predicting the Quality of SBFL -- 4 Contribution and Limitation -- 5 Summary and Outlook -- References -- Open Source Software Governance: Distilling and Applying Industry Best Practices -- 1 Introduction -- 2 Distilling Industry Best Practices -- 2.1 Getting Started with FLOSS Governance -- 2.2 Supply Chain Management -- 3 Applying Industry Best Practices -- 3.1 Case Study A -- 3.2 Case Study B -- 4 Conclusion -- References -- Dynamically Scalable Fog Architectures -- 1 Introduction -- 2 xFog: An Extension for Fog Computing -- 2.1 Fog Component -- 2.2 Fog Visibility -- 2.3 Fog Horizon -- 2.4 Fog Reachability -- 2.5 Fog Set -- 2.6 Service Constraints -- 2.7 Communication Set -- 3 xFogPlus: Dynamic and Scalable Fog Architectures -- 3.1 Dynamic Reconfigurability -- 3.2 Scalability -- 3.3 Handling Complexity -- 4 xFogStar: A Workflow for Service Provider Selection -- 5 Validation -- 6 Conclusion -- References -- Crossing Disciplinary Borders to Improve Requirements Communication -- 1 Introduction -- 2 Background and Improvement Goals -- 2.1 Requirements Artifacts -- 2.2 Practical Improvement Goals -- 2.3 Literature Review Activities -- 3 Solution Idea and Research Approach -- 4 Empirical Studies -- 4.1 Research Goals and Agenda -- 4.2 Analysis of Individual Studies: Empirical Baseline -- 4.2.1 Data Analysis Strategy: An Example -- 4.2.2 Data Interpretation -- 4.3 Secondary Data Analysis: Role-Specific Views -- 4.3.1 Data Analysis Strategy: An Example -- 4.3.2 Data Interpretation -- 4.3.3 Data Utilization -- 5 Limitations and Future Work -- 6 Summary -- References -- DevOpsUse: A Community-Oriented Methodology for Societal Software Engineering -- 1 Introduction -- 2 Motivation -- 2.1 Central Hypothesis.

3.2.1 Program Pattern -- 3.2.2 Pattern Suites -- 3.2.3 Inputs and Outputs of GenE -- 3.3 Benchmark Weaving -- 3.4 MetricsWCA: Validation of GenE’s Benchmarks -- 3.5 Determining Individual Strengths and Weaknesses of Analyzers with GenE -- 3.6 Validation of the aiT WCET Analyzer -- 3.7 Related Work and Generators in the GenE Family -- 3.7.1 Making Use of Analysis Pessimism on System Level -- 4 Conclusion -- References -- Improving the Model-Based Systems Engineering Process -- 1 Introduction -- 2 Systems Engineering Process at Daimler AG -- 2.1 Current Development Process at Daimler AG -- 2.2 Improving the Development Process at Daimler AG -- 3 Creating C&amp -- C High-Level Designs Based on Requirements -- 4 Automatic Structural Consistency Checks for Design Models -- 5 Satisfaction Verification Between Design and Functional Model -- 6 Creating C&amp -- C Functional Models Efficiently with EmbeddedMontiArc -- 7 Enriching C&amp -- C Functional Models with Extra-Functional Properties in a Consistent Way -- 8 Automatic Extra-Functional Property Verification Between Design and Functional Models -- 9 Conclusion -- References -- Understanding How Pair Programming Actually Works in Industry: Mechanisms, Patterns, and Dynamics -- 1 Introduction -- 2 Overview of Pair Programming Research -- 2.1 Quantitative Pair Programming Studies: Findings and Problems -- 2.2 Qualitative Pair Programming Studies: Findings and Problems -- 3 Research Goal, Data, and Method -- 4 Results: How Does Pair Programming Work? -- 4.1 Fluency and Togetherness -- 4.2 Knowledge Wants, Knowledge Needs, and Prototypical Dynamics -- 4.3 Practical Applications -- 5 Summary and Outlook -- References.

2.2 Research Background -- 3 DevOpsUse Methodology -- 3.1 Continuous Innovation -- 3.2 Collaborative Modeling -- 3.3 Monitoring -- 3.4 Connecting the DevOpsUse Life Cycle -- 4 Methodological and Technical Evaluation -- 4.1 Technology Evolution -- 4.2 Best Practice Guidelines -- 4.3 Application in Industry 4.0 -- 5 Conclusion -- References -- Hybrid Differential Software Testing -- 1 Introduction -- 2 Hybrid Differential Testing: Assumptions and Concept -- 3 Differential Fuzzing -- 4 Differential Dynamic Symbolic Execution -- 5 General Framework for Hybrid Differential Software Testing -- 6 Applications -- 6.1 Regression Analysis (A1) -- 6.2 Worst-Case Complexity Analysis (A2) -- 6.3 Side-Channel Analysis (A3) -- 6.4 Robustness Analysis of Neural Networks (A4) -- 7 Conclusion and Future Work -- References -- Ever Change a Running System: Structured Software Reengineering Using Automatically Proven-Correct Transformation Rules -- 1 Introduction -- 2 Abstract Execution -- 2.1 Specifying Abstract Programs -- 2.2 Symbolic Execution of Abstract Program Elements -- 3 The REFINITY Workbench -- 4 Correctness of Refactoring Rules -- 5 Restructuring for Parallelization -- 6 Cost Analysis of Transformation Rules -- 7 Conclusion and Future Work -- References -- Static Worst-Case Analyses and Their Validation Techniques for Safety-Critical Systems -- 1 Introduction -- 2 Worst-Case Analyses -- 2.1 Background and System Model -- 2.1.1 Analysis Pessimism -- 2.1.2 System Model -- 2.2 Problem Statement of WCEC Analysis -- 2.3 SysWCEC: Whole-System WCEC Analysis -- 2.3.1 Decomposition: Power Atomic Basic Blocks -- 2.3.2 Path Exploration: Power-State-Transition Graph -- 2.3.3 ILP Formulation -- 2.3.4 Cost Modeling -- 3 Validation of Worst-Case Analyses -- 3.1 Problem Statement of Validating Worst-Case Analyses -- 3.2 GenE: Benchmark Generator for WCET Tools.

3.2.1 Program Pattern -- 3.2.2 Pattern Suites -- 3.2.3 Inputs and Outputs of GenE -- 3.3 Benchmark Weaving -- 3.4 MetricsWCA: Validation of GenE’s Benchmarks -- 3.5 Determining Individual Strengths and Weaknesses of Analyzers with GenE -- 3.6 Validation of the aiT WCET Analyzer -- 3.7 Related Work and Generators in the GenE Family -- 3.7.1 Making Use of Analysis Pessimism on System Level -- 4 Conclusion -- References -- Improving the Model-Based Systems Engineering Process -- 1 Introduction -- 2 Systems Engineering Process at Daimler AG -- 2.1 Current Development Process at Daimler AG -- 2.2 Improving the Development Process at Daimler AG -- 3 Creating C& -- C High-Level Designs Based on Requirements -- 4 Automatic Structural Consistency Checks for Design Models -- 5 Satisfaction Verification Between Design and Functional Model -- 6 Creating C& -- C Functional Models Efficiently with EmbeddedMontiArc -- 7 Enriching C& -- C Functional Models with Extra-Functional Properties in a Consistent Way -- 8 Automatic Extra-Functional Property Verification Between Design and Functional Models -- 9 Conclusion -- References -- Understanding How Pair Programming Actually Works in Industry: Mechanisms, Patterns, and Dynamics -- 1 Introduction -- 2 Overview of Pair Programming Research -- 2.1 Quantitative Pair Programming Studies: Findings and Problems -- 2.2 Qualitative Pair Programming Studies: Findings and Problems -- 3 Research Goal, Data, and Method -- 4 Results: How Does Pair Programming Work? -- 4.1 Fluency and Togetherness -- 4.2 Knowledge Wants, Knowledge Needs, and Prototypical Dynamics -- 4.3 Practical Applications -- 5 Summary and Outlook -- References.

001896680

express

(Au-PeEL)EBL6898804

(MiAaPQ)EBC6898804

(OCoLC)1301449871