DOCSLIB.ORG

IDEF5 Method Report

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
IDEF5 Method Report Information Integration for Concurrent Engineering (IICE) IDEF5 Method Report Prepared for: Armstrong Laboratory AL/HRGA Wright-Patterson Air Force Base, Ohio 45433 Prepared by: Knowledge Based Systems, Inc. 1408 University Drive East College Station, Texas 77840 (409) 260-5274 Revision Date: September 21, 1994 Contract Number: F33615-C-90-0012 Information Integration for Concurrent Engineering (IICE) IDEF5 Method Report This document was prepared by the IDEF5 Method Development Team for the IICE Methods Engineering Thrust. Contributors: Perakath C. Benjamin, Ph.D. Christopher P. Menzel, Ph.D. Richard J. Mayer, Ph.D. Florence Fillion Michael T. Futrell Paula S. deWitte, Ph.D. Madhavi Lingineni Date: September 21, 1994 i Preface This document provides a comprehensive description of the IDEF5 Ontology Description Capture Method. The IDEF5 method was developed under the Information Integration for Concurrent Engineering (IICE) project, F33615-90-C-0012, funded by Armstrong Laboratory, Logistics Research Division, Wright-Patterson Air Force Base, Ohio 45433, under the technical direction of Captain JoAnn Sartor and Mr. James McManus. The prime contractor for IICE is Knowledge Based Systems, Inc. (KBSI), College Station, Texas. The authors wish to acknowledge and extend special thanks to the following people who helped compose this document: Julie Holden James MacDougall Richard McGuire ii Table of Contents Preface ............................................................................................................................................ii Table of Contents ...........................................................................................................................iii List of Figures ...............................................................................................................................vii 1 Executive Summary ............................................................................................................ 1 1.1 Motivations..................................................................................................................2 1.1.1 Motivations for Ontology.................................................................................. 5 1.1.2 Motivations for an Ontology Development Method ......................................... 6 1.2 Benefits of Ontology Development ............................................................................. 7 1.3 Overview of the Report................................................................................................ 7 1.4 The Connection Between IDEF5 and Other Methods ............................................... 10 2 Conceptual Foundations of Ontology................................................................................ 12 2.1 The Nature of Ontological Inquiry............................................................................. 12 2.2 The Central Concepts of Ontology ............................................................................ 13 2.2.1 Kinds ............................................................................................................... 13 2.2.2 Kinds as Distinguished Properties................................................................... 15 2.2.3 Contrasting Properties and Attributes ............................................................. 16 2.2.4 Relations.......................................................................................................... 17 2.2.5 Second-order Properties and Relations ........................................................... 17 2.2.6 Two Ways to Introduce Kinds into an Ontology............................................. 19 2.2.7 Parts, Wholes, and Complex Kinds................................................................. 19 2.2.8 Processes, States, and Process Kinds .............................................................. 20 2.3 Levels of Ontologies.................................................................................................. 21 2.4 On the Need for a Separate Ontology Modeling Method .......................................... 23 3 The IDEF5 Ontology Development Process ..................................................................... 25 3.1 Organize and Define the Project................................................................................ 27 3.1.1 Organize the Project ........................................................................................ 27 3.1.2 Define the Project............................................................................................ 28 3.2 Collect Data ............................................................................................................... 33 3.2.1 Interview Guidelines ....................................................................................... 33 3.2.2 Protocol Analysis ............................................................................................ 35 iii 3.2.3 Data Collection Documents ............................................................................ 35 3.3 Analyze Data.............................................................................................................. 44 3.4 Develop Initial Ontology ........................................................................................... 46 3.4.1 Develop Proto-Concepts ................................................................................. 46 3.4.2 Develop Proto-Kinds....................................................................................... 46 3.4.3 Identify Proto-Characteristics.......................................................................... 48 3.4.4 The Role of IDEF5 Schematics in Ontology Visualization ............................ 50 3.4.5 Using Classification Schematics for Ontology Development......................... 50 3.4.6 Kinds Versus Properties.................................................................................. 51 3.4.7 Coining Terms................................................................................................. 51 3.4.8 Other Guidelines ............................................................................................. 52 3.4.9 Develop Proto-Relations ................................................................................. 53 3.4.10 Role of Relation Schematics in Ontology Development................................. 56 3.4.11 Role of Composition Schematics in Ontology Development ......................... 57 3.5 Refine and Validate Ontology ................................................................................... 58 3.5.1 Kind Refinement Procedure............................................................................ 58 3.5.2 Relation Refinement Procedure ...................................................................... 60 4 The IDEF5 Ontology Languages....................................................................................... 63 4.1 The IDEF5 Schematic Language ............................................................................... 64 4.1.1 The Schematic Language Lexicon .................................................................. 64 4.1.2 IDEF5 Schematics and their Interpretation ..................................................... 66 4.2 The IDEF5 Elaboration Language ........................................................................... 102 4.2.1 Overview ....................................................................................................... 102 4.2.2 Description of the Language ......................................................................... 103 A.1 Classification Relations ........................................................................................... 125 A.1.1 Overview ....................................................................................................... 125 A.1.2 Relation Definition........................................................................................ 126 A.1.3 Relation Characterization ........................................................................................ 127 A.1.3.1 General axioms.............................................................................................. 127 A.1.3.2 Reflexivity..................................................................................................... 128 A.1.3.3 Antisymmetry................................................................................................ 128 A.1.3.4 Transitivity .................................................................................................... 128 A.2 Meronymic Relations ...................................................................................................... 129 A.2.1 Overview ..................................................................................................................... 129 iv A.2.2 Relation Definition .................................................................................................. 129 A.2.3 Relation Characterization ........................................................................................ 131 A.2.3.1 Mutual Exclusivity........................................................................................ 131 A.2.3.2 Irreflexivity.................................................................................................... 132 A.2.3.3 Asymmetry .................................................................................................... 132 A.2.3.4 Transitivity ...................................................................................................
Load more

Recommended publications
  • Sysml Distilled: a Brief Guide to the Systems Modeling Language
    ptg11539604 Praise for SysML Distilled “In keeping with the outstanding tradition of Addison-Wesley’s techni- cal publications, Lenny Delligatti’s SysML Distilled does not disappoint. Lenny has done a masterful job of capturing the spirit of OMG SysML as a practical, standards-based modeling language to help systems engi- neers address growing system complexity. This book is loaded with matter-of-fact insights, starting with basic MBSE concepts to distin- guishing the subtle differences between use cases and scenarios to illu- mination on namespaces and SysML packages, and even speaks to some of the more esoteric SysML semantics such as token flows.” — Jeff Estefan, Principal Engineer, NASA’s Jet Propulsion Laboratory “The power of a modeling language, such as SysML, is that it facilitates communication not only within systems engineering but across disci- plines and across the development life cycle. Many languages have the ptg11539604 potential to increase communication, but without an effective guide, they can fall short of that objective. In SysML Distilled, Lenny Delligatti combines just the right amount of technology with a common-sense approach to utilizing SysML toward achieving that communication. Having worked in systems and software engineering across many do- mains for the last 30 years, and having taught computer languages, UML, and SysML to many organizations and within the college setting, I find Lenny’s book an invaluable resource. He presents the concepts clearly and provides useful and pragmatic examples to get you off the ground quickly and enables you to be an effective modeler.” — Thomas W. Fargnoli, Lead Member of the Engineering Staff, Lockheed Martin “This book provides an excellent introduction to SysML.
    [Show full text]
  • Modelling, Analysis and Design of Computer Integrated Manueactur1ng Systems
    MODELLING, ANALYSIS AND DESIGN OF COMPUTER INTEGRATED MANUEACTUR1NG SYSTEMS Volume I of II ABDULRAHMAN MUSLLABAB ABDULLAH AL-AILMARJ October-1998 A thesis submitted for the DEGREE OP DOCTOR OF.PHILOSOPHY MECHANICAL ENGINEERING DEPARTMENT, THE UNIVERSITY OF SHEFFIELD 3n ti]S 5íamc of Allai]. ¿Hoot (gractouo. iHHoßt ¿Merciful. ACKNOWLEDGEMENTS I would like to express my appreciation and thanks to my supervisor Professor Keith Ridgway for devoting freely of his time to read, discuss, and guide this research, and for his assistance in selecting the research topic, obtaining special reference materials, and contacting industrial collaborations. His advice has been much appreciated and I am very grateful. I would like to thank Mr Bruce Lake at Brook Hansen Motors who has patiently answered my questions during the case study. Finally, I would like to thank my family for their constant understanding, support and patience. l To my parents, my wife and my son. ABSTRACT In the present climate of global competition, manufacturing organisations consider and seek strategies, means and tools to assist them to stay competitive. Computer Integrated Manufacturing (CIM) offers a number of potential opportunities for improving manufacturing systems. However, a number of researchers have reported the difficulties which arise during the analysis, design and implementation of CIM due to a lack of effective modelling methodologies and techniques and the complexity of the systems. The work reported in this thesis is related to the development of an integrated modelling method to support the analysis and design of advanced manufacturing systems. A survey of various modelling methods and techniques is carried out. The methods SSADM, IDEFO, IDEF1X, IDEF3, IDEF4, OOM, SADT, GRAI, PN, 10A MERISE, GIM and SIMULATION are reviewed.
    [Show full text]
  • Applications of Reinforcement Learning to Routing and Virtualization in Computer Networks
    Applications of Reinforcement Learning to Routing and Virtualization in Computer Networks by Soroush Haeri B. Eng., Multimedia University, Malaysia, 2010 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the School of Engineering Science Faculty of Applied Science © Soroush Haeri 2016 SIMON FRASER UNIVERSITY Spring 2016 All rights reserved. However, in accordance with the Copyright Act of Canada, this work may be reproduced without authorization under the conditions for “Fair Dealing.” Therefore, limited reproduction of this work for the purposes of private study, research, criticism, review and news reporting is likely to be in accordance with the law, particularly if cited appropriately. Abstract Computer networks and reinforcement learning algorithms have substantially advanced over the past decade. The Internet is a complex collection of inter-connected networks with a numerous of inter-operable technologies and protocols. Current trend to decouple the network intelligence from the network devices enabled by Software-Defined Networking (SDN) provides a centralized implementation of network intelligence. This offers great computational power and memory to network logic processing units where the network intelligence is implemented. Hence, reinforcement learning algorithms viable options for addressing a variety of computer networking challenges. In this dissertation, we propose two applications of reinforcement learning algorithms in computer networks. We first investigate the applications of reinforcement learning for deflection routing in buffer- less networks. Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched (OBS) networks. We present a framework that introduces intelligence to deflection routing (iDef).
    [Show full text]
  • H)EF3 Technical Report Version 1.0 Chard J. Mayer Christopher P
    L. H)EF3 Technical Report Version 1.0 _| i L, _chard J. Mayer Christopher P. Menzel : i_:_pa_la S.D. Mayer Know!edge Based Systems Laboratory L Depar tment- 0 fin-dustrial Engineering Texas A&M University College Station TX 77843 Reviewed by Michad-K, Painter, Capt, USAF Armstrong Laboratory Logistics Research Division Wright-Patterson =Air Force Base, Ohio 45433-6503 :_ _=_January 1991 i _: ?_ | - , i (NASA-CR-I90279) {RESEARCH ACCOMPLISHED AT N92-26587 THE KNOWLEDGE BASED SYSTEMS LAB: IDEF3, VERSION 1.0] (Texas A&M Univ.) 56 p Unclag G]/_I 0086873 L == ; IDEF3 Technical Report Version 1.0 Richard J. Mayer Christopher P. Menzel Paula S.D. Mayer Knowledge Based Systems Laboratory Department of Industrial Engineering Texas A&M University College Station TX 77843 Reviewed by Michael K. Painter, Capt, USAF Armstrong Laboratory Logistics Research Division Wright-Patterson Air Force Base, Ohio 45433-6503 January 1991 umd w _- :7. m w Preface This paper describes the research accomplished at the Knowledge Based Systems Laboratory of the Department of Industrial Engineering at Texas A&M University. Funding for the Laboratory's research in Integrated Information System Development Methods and Tools has been provided by the Air Force Armstrong Laboratory, Logistics Research Division, AFWAL/LRL, Wright-Patterson Air Force Base, Ohio 45433, under the technical direction of USAF Captain Michael K. Painter, under subcontract through the NASA RICIS Program at the University of Houston. The authors and the design team wish to acknowledge the technical insights and ideas provided by Captain Painter in the performance of this research as well as his assistance in the preparation of this report.
    [Show full text]
  • Collaboration Analytics Need More Comprehensive Models and Methods. an Opinion Paper Areum Han1, Florian Krieger2, Samuel Greiff3
    Volume 8(1), 13–29. http://doi.org/10.18608/jla.2021.7288 Collaboration Analytics Need More Comprehensive Models and Methods. An Opinion Paper Areum Han1, Florian Krieger2, Samuel Greiff3 Abstract As technology advances, learning analytics is expanding to include students’ collaboration settings. Despite their increasing application in practice, some types of analytics might not fully capture the comprehensive educational contexts in which students’ collaboration takes place (e.g., when data is collected and processed without predefined models, which forces users to make conclusions without sufficient contextual information). Furthermore, existing definitions and perspectives on collaboration analytics are incongruent. In light of these circumstances, this opinion paper takes a collaborative classroom setting as context and explores relevant comprehensive models for collaboration analytics. Specifically, this paper is based on Pei-Ling Tan and Koh’s ecological lens (2017, Situating learning analytics pedagogically: Towards an ecological lens. Learning: Research and Practice, 3(1), 1–11. https://doi.org/10.1080/23735082.2017.1305661), which illustrates the co-emergence of three interactions among students, teachers, and content interwoven with time. Moreover, this paper suggests several factors to consider in each interaction when executing collaboration analytics. Agendas and recommendations for future research are also presented. Notes for Practice • Studies of educational contexts using collaboration analytics are being implemented, but some analytics practices may unintentionally fail to fully capture the comprehensive context (e.g., when users without sufficient contextual information must make final decisions based on data collected without predefined learning models). Furthermore, definitions and perspectives on collaboration analytics vary across disciplines and underlying perspectives on collaboration.
    [Show full text]
  • Product Realization Process Modeling
    NAT’L INST. OF STAND & .TECH R I.C III! Ill" I" 'I mill mill II ml II II AlllDM NISTIR 5745 Product Realization Process Modeling: A study of requirements, methods and and research issues Kevin W. Lyons U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology Gaithersburg, MD 20899 Michael R. Duffey Richard C. Anderson George Washington University QC 100 NIST .056 NO. 5745 1995 Product Realization Process Modeling: A study of requirements, methods and and research issues Kevin W. Lyons U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology Gaithersburg, MD 20899 Michael R. Duffey Richard C. Anderson George Washington University June 1995 c U.S. DEPARTMENT OF COMMERCE Ronald H. Brown, Secretary TECHNOLOGY ADMINISTRATION Mary L. Good, Under Secretary for Technology NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY Arati Prabhakar, Director . r•^ - . 'it'/ ' ' ' ' - ' ' -•*'1 X' . r • ^.,' v./f/v ’• '^^,. • T,?. - ^ •' V.y' ’ ' :AIa ' / ><- l> r / PRP Modeling: Page 2 1. Introduction 7 2. Definition of a PRP Model 8 3. Overview of PRP Methods and Modeling Issues in Manufacturing Industries 9 3.1 PERT-based Models 9 3.2 IDEF-based Models 10 3.3 Traditional PRP Modeling Practices 12 3.4 Emerging PRP Model Applications in Industry 14 3.5 Industry Requirements for PRP Models 14 4. Modeling Issues for Advanced PRP Computer Tools 16 4. 1 Activity Network Representations 17 4.2 Representing Design Iteration and Activity "Overlapping" 18 4.3 Uncertainty Modeling of a PRP 20 4.3.1 Some Simulation Considerations 20 4.3.2 Activity Duration Uncertainty 21 4.3.3 Concurrent Activities and Stochastic Modeling 22 4.3.4 Alternative Representations of Uncertainty 23 4.4 Representing Economic Information 24 4.5 Data Collection and Validation Issues for PRP Models 27 4.6 Knowledge-Based Representations in PRP Models 28 5.
    [Show full text]
  • Multi-Level Modeling of Complex Socio-Technical Systems Report No
    Multi-Level Modeling of Complex Socio-Technical Systems Report No. CCSE-2013-01 Dr. William B. Rouse, Stevens Institute of Technology Dr. Douglas A. Bodner, Georgia Institute of Technology Report No. CCSE-2013-01 June 11, 2013 Copyright © 2013 Stevens Institute of Technology, Systems Engineering Research Center This material is baseD upon work supporteD, in whole or in part, by the U.S. Department of Defense through the Systems Engineering Research Center (SERC) unDer Contract H98230-08-D-0171. SERC is a feDerally funDeD University AffiliateD Research Center manageD by Stevens Institute of Technology The authors gratefully acknowledge the helpful comments anD suggestions of John Casti anD HarolD Sorenson Any opinions, finDings anD conclusions or recommenDations expresseD in this material are those of the author(s) anD Do not necessarily reflect the views of the UniteD States Department of Defense. NO WARRANTY THIS STEVENS INSTITUTE OF TECHNOLOGY AND SYSTEMS ENGINEERING RESEARCH CENTER MATERIAL IS FURNISHED ON AN “AS-IS” BASIS. STEVENS INSTITUTE OF TECHNOLOGY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. STEVENS INSTITUTE OF TECHNOLOGY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. Center for Complex Systems & Enterprises Report No. CCSE-2013-01 June 11, 2013 2 ABSTRACT This report presents a conceptual framework for multi-level moDeling of complex socio- technical systems, proviDes linkages to the historical roots anD technical unDerpinnings of this framework, anD outlines a catalog of component moDels for populating multi-level moDels.
    [Show full text]
  • IDEF Methods for BPR to Support CALS
    IDEF Methods for BPR to Support CALS Comprehensive Solutions for a Complex World IDEF and CALS • CALS technologies enable paperless environments or the electronic flow of information. •The implementation of CALS technologies require a re-engineering of enterprises. IDEF and BPR • Business Process Reengineering (BPR) assists in re-engineering the enterprises and the successful implementation of CALS. • IDEF Methods support BPR activities (e.g., knowledge acquisition, As-Is analysis, To -Be design, project planning, and implementation). What are Methods? Methods: A structured approach to capturing knowledge that maximizes accuracy but is also flexible enough to capture the real-world characteristics of that knowledge. What are IDEF Methods? Integration DEFinition methods Knowledge Acquisition, Analysis, and Design tools Languages that include both graphics (diagrams) and text Formal procedures for constructing models or ditifdescriptions of a parti tilcular aspect tf of an organization Why IDEF? IDEF: The IDEF Family of Methods was co- developed by industry and government. Their pppurpose is to provide a com prehensive yet flexible framework for describing, analyzing, and evaluatinggp business practices. The y are not proprietary and are supported by international standards. Characteristics of an IDEF Method Designed to address specific aspects of a problem, or provide different perspectives of the same problem Provide an explicit mechanism for integrating the results of the application of one IDEF with another Embody the knowledge
    [Show full text]
  • Improved Product, Process and Rationale Representation and Information Organisation to Support Design Learning
    INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, ICED’07 28 - 31 AUGUST 2007, CITE DES SCIENCES ET DE L'INDUSTRIE, PARIS, FRANCE IMPROVED PRODUCT, PROCESS AND RATIONALE REPRESENTATION AND INFORMATION ORGANISATION TO SUPPORT DESIGN LEARNING M D Giess 1, Y M Goh 1, L Ding 1 and C A McMahon 1 1University of Bath, UK ABSTRACT Many companies are moving from a product provision business model to a product-service model, where the producing company retains ownership of the product and supports it through life. In such a scenario it becomes possible to obtain a greater richness of information relating to product performance. However, in order for this information to be usefully deployed in improving design and manufacturing practices it is necessary to reconsider the means by which the design record is represented and organised such that linkages between the design record and the emergent outcomes as seen in service are explicit. In this manner, design episodes which continually lead to suboptimal or undesirable performance in service may be identified and remedial action taken. Current representation practices focus upon a generally topological or geometric depiction of product, structured according to the Bill of Materials, which is supported by primarily textual depictions of the process followed and rationale employed. Although an engineer may be able to retrieve and assimilate information contained within these representations, it is difficult to provide a unified view of a specific episode as distinct elements within these representations
    [Show full text]
  • Information to Users
    Group data modeling support for business process reengineering. Item Type text; Dissertation-Reproduction (electronic) Authors Lee, James David. Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 07/10/2021 07:23:08 Link to Item http://hdl.handle.net/10150/187368 INFORMATION TO USERS This manuscript,has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete mannscript and there are missing pages, these will be noted Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manusaipt have been reproduced xerograpbically in this copy.
    [Show full text]
  • A Feature-Based Survey of Model View Approaches Hugo Bruneliere, Erik Burger, Jordi Cabot, Manuel Wimmer
    A Feature-based Survey of Model View Approaches Hugo Bruneliere, Erik Burger, Jordi Cabot, Manuel Wimmer To cite this version: Hugo Bruneliere, Erik Burger, Jordi Cabot, Manuel Wimmer. A Feature-based Survey of Model View Approaches. Software and Systems Modeling, Springer Verlag, 2019, 18 (3), pp.1931-1952. 10.1007/s10270-017-0622-9. hal-01590674 HAL Id: hal-01590674 https://hal.inria.fr/hal-01590674 Submitted on 19 Sep 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Software and Systems Modeling (SoSyM) manuscript No. (will be inserted by the editor) A Feature-based Survey of Model View Approaches Hugo Bruneliere · Erik Burger · Jordi Cabot · Manuel Wimmer Received: date / Accepted: date Abstract When dealing with complex systems, information is very often frag- mented across many different models expressed within a variety of (modeling) languages. To provide the relevant information in an appropriate way to dif- ferent kinds of stakeholders, (parts of) such models have to be combined and potentially revamped by focusing on concerns of particular interest for them. This work has been partially funded by the MoNoGe national collaborative project (French FUI #15), the Electronic Component Systems for European Leadership (ECSEL) Joint Un- dertaking & the European Union’s Horizon 2020 research/innovation program under grant agreement No.
    [Show full text]
  • Software Development Methods and Usability : a Systematic Literature Review
    Linköping University | IDA 30 hp/Master Thesis |Computer Science Autumn term 2017 | LIU-IDA/LITH-EX-A—17/055--SE Software Development Methods and Usability : A Systematic Literature Review Prabhu Raj Prem Kumar Tutor, Johan Åberg, Linköpings universitet Examiner, Kristian Sandahl, Linköpings universitet 2 Final Thesis Software Development Methods and Usability: A Systematic Literature Review by Prabhu Raj Prem Kumar [email protected] Supervisor: Johan Åberg, Linköpings universitet Examiner: Kristian Sandahl, Linköpings universitet Linköping, 4th December, 2017 3 Upphovsrätt Detta dokument hålls tillgängligt på Internet – eller dess framtida ersättare – under en längre tid från publiceringsdatum under förutsättning att inga extra-ordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka kopior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervisning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säkerheten och tillgängligheten finns det lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsmannens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/ Copyright The publishers will keep this document online on the Internet - or its possible replacement - for a considerable time from the date of publication barring exceptional circumstances.
    [Show full text]