ISO/IEC/IEEE 26512 First Edition, 2011-06-01 : ISO/IEC/IEEE Systems and software engineering -- Requirements for acquirers and suppliers of user documentation / / Institute of Electrical and Electronics Engineers

• Record Nr.: UNINA9910135461903321
• Pubbl/distr/stampa: New York, NY, USA : , : IEEE, , 2011
• ISBN: 0-7381-6583-2
• Edizione: [First edition.]
• Descrizione fisica: 1 online resource (vii, 37 pages)
• Disciplina: 005.1
• Soggetti: Software engineering
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Sommario/riassunto: ISO/IEC/IEEE 26512:2011 was developed to assist users of ISO/IEC 15288:2008 (IEEE Std 15288-2008) or ISO/IEC 12207:2008 (IEEE Std 12207-2008) to acquire or supply software user documentation as part of the software life cycle processes. It defines the documentation process from the acquirer's standpoint and the supplier's standpoint. ISO/IEC/IEEE 26512:2011 covers the requirements for information items used in the acquisition of user documentation products: the Acquisition Plan, Document Specification, Statement of Work, Request for Proposals, and the proposal. It provides an overview of the software user documentation and information management processes which may require acquisition and supply of software user documentation products and services. It addresses the preparation of requirements for software user documentation. These requirements are central to the user documentation specification and Statement of Work. It includes requirements for primary document outputs of the acquisition and supply process: the Request for Proposal and the Proposal for user documentation products and services. It also discusses the use of a Documentation Management Plan and a Document Plan as they arise in the acquisition and supply processes. ISO/IEC/IEEE 26512:2011 is independent of the software tools that may be used to produce documentation, and applies to both printed documentation and on-; screen documentation. Much of its guidance is applicable to user documentation for systems including hardware as well as software. Keywords: acquisition, information management, proposal, software user documentation, statement of work, supply.

Design Science Research for a Resilient Future : 19th International Conference on Design Science Research in Information Systems and Technology, DESRIST 2024, Trollhättan, Sweden, June 3-5, 2024, Proceedings

• Record Nr.: UNISA996601561603316
• Autore: Mandviwalla Munir
• Pubbl/distr/stampa: Cham : , : Springer, , 2024; ©2024
• ISBN: 9783031611759; 9783031611742
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (464 pages)
• Collana: Lecture Notes in Computer Science Series ; ; v.14621
• Altri autori (Persone): SöllnerMatthias; TuunanenTuure
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- DSR for a Resilient World (Theme Track) -- Disentangling the Problem Space: A Validated Problem Statement for Sustainability Support Systems -- 1 Introduction -- 2 Background -- 3 Research Design -- 4 The Problem Analysis Echelon -- 4.1 Understanding the Practitioner's Problem -- 4.2 Abstracting Sustainable Behavior -- 4.3 Defining Our Problem Situation -- 4.4 Analyzing the Challenges Around Sustainable Behavior -- 4.5 Field Check on Challenges and Solvability of Resource Conservation -- 4.6 Facilitating Sustainable Behavior with Behavioral Interventions -- 4.7 Analyzing the Challenges in the Behavioral Intervention Literature -- 4.8 The Mechanisms for Encouraging Sustainable Behavior -- 4.9 Validated Problem Statement as Design Knowledge -- 5 Discussion -- References -- Let Citizens Speak Up: Designing Intelligent Online ; Participation for Urban Planning -- 1 Introduction -- 2 Related Work -- 2.1 Online Participation in Urban Planning -- 2.2 Online Participation in Urban Planning -- 3 Research Approach -- 4 Awareness of the Problem -- 5 Suggestion -- 6 Development -- 7 Evaluation -- 8 Findings -- 9 Discussion -- 10 Conclusion -- References -- Overcoming Rebound Effects: A Process Blueprint for Circular Systems Design -- 1 Introduction -- 2 Theoretical Background -- 2.1 Circular Design Strategies -- 2.2 Rebound Effects -- 3 Method -- 3.1 Problem Definition -- 3.2 Design Creation and Evaluation -- 4 Results -- 4.1 Problem Identification and Objective Definition -- 4.2 Artifact Development -- 4.3 Artifact Demonstration and Evaluation -- 4.4 Adaptation of the Design Tool -- 5 Discussion -- 6 Conclusions, Limitations and Future Research -- References -- General Track -- Conceptualizing Multi-party AI Reliance for Design Research -- 1 Introduction -- 2 Background -- 2.1 AI Reliance.; 2.2 (Cognitive) Balance Theory -- 3 Conceptualization of MPAIR -- 3.1 Reliance Balance -- 3.2 Multi-party AI Reliance -- 4 MPAIR in Practice -- 4.1 Example A: Conversational Agents in Financial Advisory Service Encounters -- 4.2 Example B: AI-Based Price Estimations in Used Car Market Negotiations -- 4.3 Conclusion -- 5 Discussion -- 5.1 The Importance of Single-User Appropriate Reliance -- 5.2 Design Principle for MPAIR -- 5.3 Future Extensions of MPAIR -- 6 Conclusion -- References -- Wasn't Expecting that - Using Abnormality as a Key to Design a Novel User-Centric Explainable AI Method -- 1 Introduction -- 2 Background and Related Literature -- 2.1 Counterfactual Explanations -- 2.2 Abnormality in Explanations -- 2.3 Abnormality in Counterfactual Explanations -- 3 A Novel XAI Method to Generate Abnormal Counterfactual Explanations -- 3.1 Abnormality Measure Based on KDE -- 3.2 Integration of the Abnormality Measure into an XAI Method to Generate Counterfactual Explanations -- 4 Demonstration and Evaluation -- 4.1 Case Setting and Data Set -- 4.2 Instantiation of ACE and a Competing Artifact -- 4.3 Functionally-Grounded Evaluation -- 4.4 Human-Grounded Evaluation -- 5 Implications, Limitations, and Further Research -- 5.1 Implications for Theory and Practice -- 5.2 Limitations and Further Research -- References -- Designing a Large Language Model-Based Coaching Intervention for Lifestyle Behavior Change -- 1 Introduction -- 2 Related Work -- 2.1 Motivational Interviewing -- 2.2 Large Language Models -- 3 Methodology -- 4 Objectives of a Solution -- 5 Design and Development -- 5.1 Deriving Design Principles from Theory and User Interviews -- 5.2 Development of an Initial Prompt -- 6 Demonstration -- 7 Evaluation -- 8 Discussion and Conclusion -- References -- A Design-Principle-Friendly Conceptual Model of Observational Crowdsourcing.; 1 Introduction -- 2 Background -- 2.1 Crowdsourcing: Definitions, Conceptual Models and Taxonomies -- 2.2 Design Science Research and Design Principles -- 3 Methodology -- 3.1 Identifying Relevant Literature and Inclusion Criteria -- 3.2 Analysis and Theoretical Development -- 4 Results -- 5 Discussion and Conclusion -- References -- Design Principles for Machine Learning Based Clinical Decision Support Systems: A Design Science Study -- 1 Introduction -- 2 Research Approach -- 3 The Readmission Prediction Algorithm -- 4 Exploring the Inpatient Care Process -- 5 Explainable AI and Clinical Decision Support Systems -- 5.1 Explainable Artificial Intelligence -- 5.2 Designing Clinical Decision Support Systems -- 6 User eXperience Design -- 6.1 Exploration of User Needs -- 6.2 Exploration of Stakeholders and the Inpatient Care Process -- 6.3 Exploration of Design Alternatives -- 7 Design Principles for CDSS in the Context ; of Readmission Prediction for Heart Failure Patients -- 8 Conclusions -- References -- Theory-Driven Design of a Negotiation Canvas for Reaching Win-Win Agreements -- 1 Introduction -- 2 Conceptual Background -- 2.1 Negotiations -- 2.2 Canvas Development -- 3 Methodology -- 3.1 Design Science Research -- 3.2 Evaluation Strategy -- 4 The Negotiation Canvas -- 4.1 Problem Identification and Objectives of the Negotiation Canvas -- 4.2 Design and Development of the Negotiation Canvas -- 4.3 Demonstration and Evaluation of the Negotiation Canvas -- 5 Discussion -- 5.1 Canvas Enhancement, Limitations and Future Research -- 6 Conclusion -- Appendix A. The Novice Evaluation Task -- References -- DSR Methods and Education -- A Typology of Knowledge Creation in Design Science Research Projects -- 1 Introduction -- 2 Related Literature: Design Principles and Theories -- 2.1 Foundations of Principles and Knowledge.; 3 A Typology for Knowledge Creation in DSR -- 3.1 Design Attribute Postulates -- 3.2 Design Theories -- 3.3 Good Design Practices -- 3.4 Design Principles -- 4 A Dual Use Framework of DSR Knowledge Typology -- 5 Conclusions and Future Work -- References -- Classifying Design Science Research in Terms of Types of Reasoning from an Epistemological Perspective -- 1 Introduction -- 2 Epistemological Types of Reasoning -- 2.1 Induction -- 2.2 Deduction -- 2.3 Abduction -- 3 Methodology -- 4 Discussion of Results -- 4.1 Quantitative Analysis -- 4.2 Qualitative Analysis -- 5 Generalized Framework for Reasoning in DSR -- 6 Conclusion -- References -- Visualizing Argumentation for Research Problem and Research Design -- 1 Introduction -- 2 Literature Review -- 2.1 Identification of a Research-Worthy Problem -- 2.2 Conceptual Modeling for Visualization of Research Problems -- 3 Methodology -- 4 Suggestion -- 5 Implementation -- 6 Application of the Modelling Language -- 7 Discussion -- 8 Conclusion -- References -- Toward a Method for Design Science Research Meta-Studies to Improve the Reusability of Design Principles -- 1 Introduction -- 2 Research Background -- 2.1 Design Principles and Related Work on Their Reusability -- 2.2 Meta-Studies and Systematic Reviews -- 3 Methodology -- 4 Results -- 4.1 Derivation of the Method for Design Science Research Meta-Studies -- 4.2 Evaluation in an Expert Workshop -- 4.3 Demonstration of the Method for Design Science Research Meta-Studies -- 5 Discussion -- 6 Conclusion -- References -- Dialectical Tensions in Design Theorizing: Exploring the Selection, Use, and Development of Kernel Theory -- 1 Introduction -- 2 Kernel Theory in Design Science Research -- 3 A Dialectical Inquiry Perspective on Kernel Theory -- 3.1 Pre-use: Picking Paradox -- 3.2 In-Use: Adoption Ambivalence -- 3.3 Post-use: Development Dilemma.; 4 Responses to Tensions in Kernel Theory Operationalization -- 4.1 Generic Responses to Kernel Theory Operationalization -- 4.2 Specific Responses to Kernel Theory Operationalization -- 5 Contributions, Limitations, and Outlook -- References -- Design Science Research as a Guide for Innovative Higher Education Teaching: Towards an Application-Oriented Extension of the Proficiency Model -- 1 Introduction -- 2 Theoretical Background and Related Work -- 3 Instantiating DSR for Designing Higher Education -- 4 Application-Oriented Extension of the Proficiency Model -- 5 Conclusion -- 6 Outlook -- References -- Let's Chat to Negotiate: Designing a Conversational Agent for Learning Negotiation Skills -- 1 Introduction -- 2 Conceptual Background and Related Work -- 2.1 Negotiations and the Harvard Negotiation Model -- 2.2 Learning Negotiation Skills -- 2.3 Learning Systems for Negotiation Skills -- 3 Research ; Methodology -- 4 Results -- 4.1 Problem Identification and Objectives of a Solution -- 4.2 Design and Development -- 4.3 Demonstration and Evaluation -- 5 Discussion -- 5.1 Limitations and Further Research -- 6 Contributions -- References -- DSR in Practice -- No Need to Cry over Spilt Milk: A Workflow for Regenerating Graph Data Using Robotic Process Automation -- 1 Introduction -- 2 Research Background -- 2.1 Document Image Analysis -- 2.2 Robotic Process Automation -- 2.3 Graph Digitizing Software Tools -- 2.4 Comparative Approaches -- 3 Research Method -- 4 Objectives of a Solution -- 5 Design, Development and Demonstration -- 5.1 Workflow -- 5.2 Data Understanding -- 5.3 Preprocessing -- 5.4 Extraction and Postprocessing -- 6 Evaluation and Results -- 6.1 Evaluation -- 6.2 Intercoder Reliability -- 7 Discussion and Contribution -- 7.1 Discussion -- 7.2 Contribution -- 8 Conclusion -- References.; Towards a Smarter Tomorrow: A Design Science Perspective on Building a Smart Campus IoT Data Platform.