Tropos: a Framework for Requirements-Driven Software Development
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												  Software Development Career PathwayCareer Exploration Guide Software Development Career Pathway Information Technology Career Cluster For more information about NYC Career and Technical Education, visit: www.cte.nyc Summer 2018 Getting Started What is software? What Types of Software Can You Develop? Computers and other smart devices are made up of Software includes operating systems—like Windows, Web applications are websites that allow users to contact management system, and PeopleSoft, a hardware and software. Hardware includes all of the Apple, and Google Android—and the applications check email, share documents, and shop online, human resources information system. physical parts of a device, like the power supply, that run on them— like word processors and games. among other things. Users access them with a Mobile applications are programs that can be data storage, and microprocessors. Software contains Software applications can be run directly from a connection to the Internet through a web browser accessed directly through mobile devices like smart instructions that are stored and run by the hardware. device or through a connection to the Internet. like Firefox, Chrome, or Safari. Web browsers are phones and tablets. Many mobile applications have Other names for software are programs or applications. the platforms people use to find, retrieve, and web-based counterparts. display information online. Web browsers are applications too. Desktop applications are programs that are stored on and accessed from a computer or laptop, like Enterprise software are off-the-shelf applications What is Software Development? word processors and spreadsheets. that are customized to the needs of businesses. Popular examples include Salesforce, a customer Software development is the design and creation of Quality Testers test the application to make sure software and is usually done by a team of people.
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												  Blockchain Database for a Cyber Security Learning SystemSession ETD 475 Blockchain Database for a Cyber Security Learning System Sophia Armstrong Department of Computer Science, College of Engineering and Technology East Carolina University Te-Shun Chou Department of Technology Systems, College of Engineering and Technology East Carolina University John Jones College of Engineering and Technology East Carolina University Abstract Our cyber security learning system involves an interactive environment for students to practice executing different attack and defense techniques relating to cyber security concepts. We intend to use a blockchain database to secure data from this learning system. The data being secured are students’ scores accumulated by successful attacks or defends from the other students’ implementations. As more professionals are departing from traditional relational databases, the enthusiasm around distributed ledger databases is growing, specifically blockchain. With many available platforms applying blockchain structures, it is important to understand how this emerging technology is being used, with the goal of utilizing this technology for our learning system. In order to successfully secure the data and ensure it is tamper resistant, an investigation of blockchain technology use cases must be conducted. In addition, this paper defined the primary characteristics of the emerging distributed ledgers or blockchain technology, to ensure we effectively harness this technology to secure our data. Moreover, we explored using a blockchain database for our data. 1. Introduction New buzz words are constantly surfacing in the ever evolving field of computer science, so it is critical to distinguish the difference between temporary fads and new evolutionary technology. Blockchain is one of the newest and most developmental technologies currently drawing interest.
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												  Geo-DB Requirement Analysis2.1.1. Overview Lifecycle 2 Conceptual Database Design 2.1 Requirement analysis -> Text 2.2 Modeling languages Requirement analysis -> Conceptual Model 2.1.1 Overview Conceptual Design 2.1.2 Requirement Analysis (case study) 2.2.1 Basic Modeling Primitives 2.2.2 Modeling Languages: UML and CREATE TABLE Studentin (SID INTEGER PRIMARY KEY, -> Database schema VName CHAR(20) Name CHAR(30)CREATE NOT TABLENULL, Kurs Entity-Relationship Model (ERM) Logical Schema Design Email Char(40));(KID CHAR(10) PRIMARY KEY, Name CHAR(40) NOT NULL, Dauer INTEGER); CREATE TABLE Order 2.2.3 Conceptual DB design: basics ODate DATE soldBy INTEGER FOREIGN KEY REFEREBCES Peronal(PID) 2.2.4 From Requirements to Models CID INTEGER FOREIGN KEY REFERENCES Customer (CID)); Physical Schema Design -> Access paths References: Kemper / Eickler chap 2, Elmasri / Navathe chap. 3 Administration Garcia-Molina / Ullmann / Widom: chap. 2 © HS-2009 Redesign 02-DBS-Conceptual-2 Database Design:Terminology 2.1.2 Requirement Analysis Most important: talk with your customers! Def.: Database Design The process of defining the overall structure of a database, Tasks during RA: i.e. the schema, on different layers of abstraction. Identify essential "real world" information (e.g. Design levels: Conceptual, logical, physical interviews) Remove redundant, unimportant details Includes "Analysis" and "Design" from SE Clarify unclear natural language statements DB Modeling: defining the "static model" using formal or visual languages Fill remaining gaps in discussions Distinguish data and operations DB SE Requirements Requirements Conceptual modeling Analysis Requirement analysis & Conceptual Design aims at Logical modeling Design focusing thoughts and discussions ! Physical modeling Implementation © HS-2009 02-DBS-Conceptual-3 © HS-2009 02-DBS-Conceptual-4 Example: Geo-DB Requirement Analysis The database we develop will contain data about countries, cities, Clarify unclear statements organizations and geographical facts.
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												  An Introduction to Cloud Databases a Guide for AdministratorsCompliments of An Introduction to Cloud Databases A Guide for Administrators Wendy Neu, Vlad Vlasceanu, Andy Oram & Sam Alapati REPORT Break free from old guard databases AWS provides the broadest selection of purpose-built databases allowing you to save, grow, and innovate faster Enterprise scale at 3-5x the performance 14+ database engines 1/10th the cost of vs popular alternatives - more than any other commercial databases provider Learn more: aws.amazon.com/databases An Introduction to Cloud Databases A Guide for Administrators Wendy Neu, Vlad Vlasceanu, Andy Oram, and Sam Alapati Beijing Boston Farnham Sebastopol Tokyo An Introduction to Cloud Databases by Wendy A. Neu, Vlad Vlasceanu, Andy Oram, and Sam Alapati Copyright © 2019 O’Reilly Media Inc. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more infor‐ mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Development Editor: Jeff Bleiel Interior Designer: David Futato Acquisitions Editor: Jonathan Hassell Cover Designer: Karen Montgomery Production Editor: Katherine Tozer Illustrator: Rebecca Demarest Copyeditor: Octal Publishing, LLC September 2019: First Edition Revision History for the First Edition 2019-08-19: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. An Introduction to Cloud Databases, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views.
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												  The Roots of Software Engineering*THE ROOTS OF SOFTWARE ENGINEERING* Michael S. Mahoney Princeton University (CWI Quarterly 3,4(1990), 325-334) At the International Conference on the History of Computing held in Los Alamos in 1976, R.W. Hamming placed his proposed agenda in the title of his paper: "We Would Know What They Thought When They Did It."1 He pleaded for a history of computing that pursued the contextual development of ideas, rather than merely listing names, dates, and places of "firsts". Moreover, he exhorted historians to go beyond the documents to "informed speculation" about the results of undocumented practice. What people actually did and what they thought they were doing may well not be accurately reflected in what they wrote and what they said they were thinking. His own experience had taught him that. Historians of science recognize in Hamming's point what they learned from Thomas Kuhn's Structure of Scientific Revolutions some time ago, namely that the practice of science and the literature of science do not necessarily coincide. Paradigms (or, if you prefer with Kuhn, disciplinary matrices) direct not so much what scientists say as what they do. Hence, to determine the paradigms of past science historians must watch scientists at work practicing their science. We have to reconstruct what they thought from the evidence of what they did, and that work of reconstruction in the history of science has often involved a certain amount of speculation informed by historians' own experience of science. That is all the more the case in the history of technology, where up to the present century the inventor and engineer have \*-as Derek Price once put it\*- "thought with their fingertips", leaving the record of their thinking in the artefacts they have designed rather than in texts they have written.
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												  Middleware-Based Database Replication: the Gaps Between Theory and PracticeAppears in Proceedings of the ACM SIGMOD Conference, Vancouver, Canada (June 2008) Middleware-based Database Replication: The Gaps Between Theory and Practice Emmanuel Cecchet George Candea Anastasia Ailamaki EPFL EPFL & Aster Data Systems EPFL & Carnegie Mellon University Lausanne, Switzerland Lausanne, Switzerland Lausanne, Switzerland [email protected] [email protected] [email protected] ABSTRACT There exist replication “solutions” for every major DBMS, from Oracle RAC™, Streams™ and DataGuard™ to Slony-I for The need for high availability and performance in data Postgres, MySQL replication and cluster, and everything in- management systems has been fueling a long running interest in between. The naïve observer may conclude that such variety of database replication from both academia and industry. However, replication systems indicates a solved problem; the reality, academic groups often attack replication problems in isolation, however, is the exact opposite. Replication still falls short of overlooking the need for completeness in their solutions, while customer expectations, which explains the continued interest in developing new approaches, resulting in a dazzling variety of commercial teams take a holistic approach that often misses offerings. opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. Even the “simple” cases are challenging at large scale. We deployed a replication system for a large travel ticket brokering This paper aims to characterize the gap along three axes: system at a Fortune-500 company faced with a workload where performance, availability, and administration. We build on our 95% of transactions were read-only. Still, the 5% write workload own experience developing and deploying replication systems in resulted in thousands of update requests per second, which commercial and academic settings, as well as on a large body of implied that a system using 2-phase-commit, or any other form of prior related work.
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												  Research on Programming Technology of Computer Software Engineering Database Based on Multi-Platform2019 4th International Industrial Informatics and Computer Engineering Conference (IIICEC 2019) Research on Programming Technology of Computer Software Engineering Database Based on Multi-Platform Wei Hongchang, Zhang Li Jiangxi Vocational College of Mechanical& Electronical Technology Jiangxi Nanchang 330013, China Keywords: Computers, Database, Programming, Software Engineering Abstract: With the rapid development of social science and technology, various trades and industries have also developed. For computer applications, the most important thing is the software system and hardware system in its components. As far as software engineering is concerned, in the construction of engineering chemical methods, the construction methods of engineering chemical should be combined to enhance the value of software application. The development of computer technology has formed a certain scale up to now, and it has dabbled in various fields. However, due to the different requirements of different industries on the performance of computer technology. Through the analysis of software database programming technology, in the creation of software computer structure, as a very important part, it will have a certain impact on the strength of computer computing ability. Aiming at the research of database programming technology in computer software engineering, this paper analyzes the advantages of database programming technology, and expounds the application of database programming technology in computer software engineering. 1. Introduction At the present stage, computing technology has been widely used in today's society and has penetrated into different industries in different fields. The arrival and popularization of computers have been highly valued and widely used. Through the analysis of software database programming technology, as a very important component in the creation of software computer composition structure, it will have a certain impact on the strength of computer computing ability [1].
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												  Database Technology for Bioinformatics from Information Retrieval to Knowledge SystemsDatabase Technology for Bioinformatics From Information Retrieval to Knowledge Systems Luis M. Rocha Complex Systems Modeling CCS3 - Modeling, Algorithms, and Informatics Los Alamos National Laboratory, MS B256 Los Alamos, NM 87545 [email protected] or [email protected] 1 Molecular Biology Databases 3 Bibliographic databases On-line journals and bibliographic citations – MEDLINE (1971, www.nlm.nih.gov) 3 Factual databases Repositories of Experimental data associated with published articles and that can be used for computerized analysis – Nucleic acid sequences: GenBank (1982, www.ncbi.nlm.nih.gov), EMBL (1982, www.ebi.ac.uk), DDBJ (1984, www.ddbj.nig.ac.jp) – Amino acid sequences: PIR (1968, www-nbrf.georgetown.edu), PRF (1979, www.prf.op.jp), SWISS-PROT (1986, www.expasy.ch) – 3D molecular structure: PDB (1971, www.rcsb.org), CSD (1965, www.ccdc.cam.ac.uk) Lack standardization of data contents 3 Knowledge Bases Intended for automatic inference rather than simple retrieval – Motif libraries: PROSITE (1988, www.expasy.ch/sprot/prosite.html) – Molecular Classifications: SCOP (1994, www.mrc-lmb.cam.ac.uk) – Biochemical Pathways: KEGG (1995, www.genome.ad.jp/kegg) Difference between knowledge and data (semiosis and syntax)?? 2 Growth of sequence and 3D Structure databases Number of Entries 3 Database Technology and Bioinformatics 3 Databases Computerized collection of data for Information Retrieval Shared by many users Stored records are organized with a predefined set of data items (attributes) Managed by a computer program: the database
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												  Design and Implementation of a Standards Based Ecommerce Solution in the Business-To-Business AreaDesign and implementation of a standards based eCommerce solution in the business-to-business area Thesis of Andreas Junghans Department of Computer Science Karlsruhe University of Applied Sciences, Germany Written at ISB GmbH, Karlstrasse 52-54, 76133 Karlsruhe 03/01/2000 to 08/31/2000 Supervisor at Karlsruhe University of Applied Sciences: Prof. Klaus Gremminger Co-supervisor: Prof. Dr. Lothar Gmeiner Supervisor at ISB GmbH: Dipl.Inform. Ralph Krause Declaration I have written this thesis independently, solely based on the literature and tools mentioned in the chapters and the appendix. This document - in the current or a similar form - has not and will not be submitted to any other institution apart from the Karlsruhe University of Applied Sciences to receive an academical grade. I would like to thank Ralph Krause and Gerlinde Wiest of ISB GmbH for their support over the last six month, as well as Peter Heil and Frank Nielsen of Heidelberger Druckmaschinen AG for providing informa- tion and suggestions for the requirements analysis. Acknowledgements also go to Christian Schenk for his MiKTEX distribution and the Apache group for their XML tools. Finally, I would like to thank Professor Klaus Gremminger of the Karlsruhe University of Applied Sciences for his supervision of this thesis. Karlsruhe, 3rd of September, 2000 (Andreas Junghans) Contents 1 Introduction 1 1.1 Document conventions ...................................... 1 1.2 Subject of this thesis ....................................... 1 1.3 Rough schedule .......................................... 2 2 Requirements analysis 3 2.1 Tools used ............................................. 3 2.2 Domain requirements ....................................... 3 2.2.1 Clarification of terms ................................... 3 2.2.2 Specific requirements on ”Business to Business” applications ............
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												  What Is a Database? Differences Between the Internet and LibraryWhat is a Database? Library databases are mostly full-text material (in their entirety) and summaries or descriptions of articles. They are collections of articles from newspapers, magazines and journals and electronic reference sources. Databases are selected for the quality and variety of resources they offer and are accessed using the Internet. Your library pays for you to have access to a number of relevant databases. You support this with your tuition, so get the most out of your money! You can access them from home or school via the Library Webpage or use the link below. http://www.mxcc.commnet.edu/Content/Find_Articles.asp Two short videos on the benefits of using library databases: http://www.youtube.com/watch?v=VUp1P-ubOIc http://youtu.be/Q2GMtIuaNzU Differences Between the Internet and Library Databases The Internet Library Databases Examples Google, Yahoo, Bing LexisNexis, Literary Reference Center or Health and Wellness Resource Center Review process None – anyone can add Checked for accuracy by content to the Web. publishers. Chosen by your college’s library. Includes “peer-reviewed” scholarly articles. Reliability Unknown Very No quality control mechanisms! Content Anything, from pictures of a Scholarly journal articles, Book person’s pets to personal reviews, Research papers, (usually not researched and Conference papers, and other unsubstantiated) opinions on scholarly information gun control, abortion, etc. How often updated Unknown/varies. Regularly – daily, quarterly monthly Cost “Free” but some of the info Library has paid for you to you may need for your access these databases. assignment requires a fee. Organization Very little or no organization Very organized Availability Websites come and go.
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												  Requirements PhaseRequirements Phase • Chance of a product being on time and within budget is very slim unless the development team knows what the product should do • To achieve this level the development team must analyze the needs of the client as precisely as possible Ch10 Copyright © 2004 by Eugean 1 Hacopians Requirements Phase • After a clear picture of the problem the development teams can answer the question: – What should the product do? • The process of answering this question lies within the requirements phase Ch10 Copyright © 2004 by Eugean 2 Hacopians Requirements Phase • Common misconceptions are: – During requirement phase the developer must determine what the client wants – The client can be unclear as to what they want – The client may not be able to communicate what they want to the developer • Most clients do not understand the software development process Ch10 Copyright © 2004 by Eugean 3 Hacopians Requirements Elicitation • This process is finding out a client’s requirements • Members of the requirements team must be familiar with the application domain • Requirement analysis is the process of: – Understanding the initial requirements – Refining requirements – Extending requirements Ch10 Copyright © 2004 by Eugean 4 Hacopians Requirements Elicitation • This process starts with several members of the requirements analysis team meeting with several members of the client’s team • It is essential to use the correct terminology or settle on an agreed set – Misunderstanding terminology can result in a faulty product – This could result in
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												  Data Quality Requirements Analysis and Modeling December 1992 TDQM-92-03 Richard YPublished in the Ninth International Conference of Data Engineering Vienna, Austria, April 1993 Data Quality Requirements Analysis and Modeling December 1992 TDQM-92-03 Richard Y. Wang Henry B. Kon Stuart E. Madnick Total Data Quality Management (TDQM) Research Program Room E53-320 Sloan School of Management Massachusetts Institute of Technology Cambridge, MA 02139 USA 617-253-2656 Fax: 617-253-3321 Acknowledgments: Work reported herein has been supported, in part, by MITís Total Data Quality Management (TDQM) Research Program, MITís International Financial Services Research Center (IFSRC), Fujitsu Personal Systems, Inc. and Bull-HN. The authors wish to thank Gretchen Fisher for helping prepare this manuscript. To Appear in the Ninth International Conference on Data Engineering Vienna, Austria April 1993 Data Quality Requirements Analysis and Modeling Richard Y. Wang Henry B. Kon Stuart E. Madnick Sloan School of Management Massachusetts Institute of Technology Cambridge, Mass 02139 [email protected] ABSTRACT Data engineering is the modeling and structuring of data in its design, development and use. An ultimate goal of data engineering is to put quality data in the hands of users. Specifying and ensuring the quality of data, however, is an area in data engineering that has received little attention. In this paper we: (1) establish a set of premises, terms, and definitions for data quality management, and (2) develop a step-by-step methodology for defining and documenting data quality parameters important to users. These quality parameters are used to determine quality indicators, to be tagged to data items, about the data manufacturing process such as data source, creation time, and collection method.