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Contents


    System Modeling II

      Story -- the rubber gloves

      A young and naive officer was visiting an army barracks on the South Coast of England in the 1930's. He observed that the sentry at the gate handed a pair of thick rubber gloves to the next sentry when they changed the guard. The next sentry hung the rubber gloves in the sentry box and at the end of his "shift" took them out and handed them to the incoming sentry. The officer was intrigued and asked questions. The sentry could explain what he had to do, but he was reduced to gibbering when the officer tried to find out why they did this. The sergeant, when asked "Why?", just repeated the manual which describe the procedure but didn't explain what the purpose was for. As far as the sergeant was concerned the only purpose for the rubber gloves was for the sentries to hand them on to each other! It took a lot of research in the history of the barracks to find out why.

      (I'll give the explanation in class).

      Moral1: Always document the reasons for an activity in a system. Do it when the activity is thought of, or do it 10 years later when you are forced to remove or change it.

      Moral2: Today's solution is tomorrow's confusion and the next day's problem.

      Can you give examples of Moral2 -- Today's solution.... is tomorrow's problem

      It is a good idea to have incoming EMail pop up as it arrives. This solves the problem: "I want to know when I have new Email". It is a natural solution. But in some cases it causes a problem. For example I saw a presentation at the IEEE/ACM Software Engineering conference ruined when the speaker's Email popped up in the middle of his slides. Now "Smart Phones" and "iPods" make sure the we always know when new mail arrives, at the cost of continuous distractions.

      A similar example starts with the problem that a fixed image on a cathode ray tube tends to damage it. So computers had special programs called "screen savers" that turn on and display a moving image when the the machine is idle. But, in the middle of a presentation, video, or when watching a news channel, a screen saver turning on is irritating.

      Physical vs Logical Models

      Again you can use the techniques below to model systems at two levels. You can include all the technological details. This gives you a physical model. You can also create abstract or essential models that will not change when the technology implementing them is changed.

      Topics in Systems Modeling

      We have already the two most popular diagrams for Data Modeling -- DFDs and ERDs: [ a4.html ] and this section adds to these diagrams some text based ways of "documenting a system". Later we will get to some specialized and detailed techniques: [ r1.html ] (Rules and procedures), [ r2.html ] (Requirements), and [ r3.html ] (Specifications).

      These notes cover

      1. Lists of definitions [ Glossaries ]
      2. Detailed definitions of the data, entities, stores, processes, and flows in a system [ Data Dictionaries ]
      3. Simple text descriptions of processes and procedures [ Goals and Stories ] and [ Functions and Formulas ]
      4. Sequences of steps describing a possible behavior of a system [ Scenarios ]
      5. Programs that explore possible processes [ Prototypes ]

      Glossaries

      A glossary is one of the must useful documents you can develop when analyzing an enterprise. It is just a list of definitions. But you can use a glossary to define data, words, processes, entities, stores, etc. All a glossary does is record the meanings given to the words and phrases used in an enterprise. Notice that is is common for different parts to use different terms for the same thing and the same term for different things. This one reason why you need to track both the term and its context (= where it is used). This is why I use a notation like this
       	term::context=meaning.
      when I put a glossary on the web.

      You also need to track aliases because the same idea is often referred to by different phrases in different parts of an organization.

      I have prepared a glossary for this class by extracting definitions from the web pages. Here is what part of it looks like

      You can use pieces of paper, pages in a notebook, index cards, text files (notepad, wordpad, memos, notes), web pages, spread sheets, or even a small data base to implement a glossary. Informal glossaries are easy to jot down or type. For example
      Net

      1. user_id::CSE="unique identifier of a user".
      2. user_name::TNS=user_id.
      3. CSE::CSUSB="Computer Science and Engineering Department".
      4. TNS::CSUSB="Telecommunications and Network Services".
      5. CSUSB::="California State University, San Bernardino".

      (End of Net)

      I use this syntax
       		defined_term :: context = definition.
      Notice that I also define the contexts for each definition. Contexts start to analyze the structure of the organization.

      In Computer Science, in the mid-60's, we adopted the idea of listing definitions to define the syntax or grammar of computer languages. Jim Bachus and Pete Naur proposed the original Bachus-Naur-Form or BNF as a practical form of the theoretical "context free grammars" developed by Noam Chomsky. Since then just about every programming language has had its syntax defined by using EBNF (Extended BNF) that has ways to show repeated and optional patterns in the data.

      If you want you can look at [ EBNF ] on the Wikipedia for history, examples, and details. I have many examples -- see the [ Online Enrichment ] exercises at the end of this page.

      BNF was adapted to define data by Tom DeMarco as part of Structured Analysis. You would define, for example,

    1. login::= user_id password, user supplies their id and password when they login.
    2. user_id::=char[1..8], between 1 to 8 characters, inclusive.
    3. password::=char[*], any number of characters.

      My XBNF notation uses a more mathematical format based on EBNF that includes discrete mathematics. For example you can define

    4. SSN::data_element=("0".."9")^9, unique identifier for USA residents.
    5. date::USA= month "/" day "/" year.
    6. date::UK=day "/" month "/" year.

      XBNF is defined as a computer language. As a result I have tools for handling it. If you go to [ ../hole.html ] you will find a web app that will convert XBNF into HTML.

      I generated a complete glossary [ glossary.html ] of all the materials for this course by extracting and sorting all the definitions on all the pages. You might like to look at it.

      A glossary can grow into a data dictionary if you add detailed information on all the processes, external entities, and data for the enterprise. This in turn becomes the information you must have to design new systems. Here for example is part of the data dictionary for a recent student project.
      Net

      1. AddDate::element =Date .
      2. AddMoney::element =Money .
      3. Address::element =Varchar .
      4. AddTimes::element =Time .
      5. ...

      (End of Net)

      See the online Enrichment at the bottom of this page to see the whole glossary/data dictionary.

      Data Dictionaries

        What is a data dictionary

        A data dictionary is a complete description, often in a data base, that describes a data base. These are commonly part of the DBMS -- Data Base Management System. A data dictionary is any manual or computerized system that lets you record, read, access, and organize information about the data and processes in a system. Like any dictionary it defines the meanings of words and phrases. Often, the information about the data is placed in tables with a fixed layout. In this case the words and phrases in the dictionary describe data and processing. For example you might find
        Table
        LabelTypeDescription
        SSNdata element9 digits uniquely identifying humans in the USA.

        (Close Table)
        in an informal dictionary.

        See [ Data_dictionary ] (the Wikipedia entry).

        Most of the data dictionaries on the market use a data base rather a set of simple files. They are typically part of a proprietary DBMS. Rather than promote a particular DBMS in this course I will describe data dictionaries in terms of tables.

        All data dictionaries must define the four levels of data:

        1. Element or Item
        2. Record or Row
        3. File or Table
        4. Data Base

        Each level is made up of the lower levels: a record has elements for example.

        Who developed data dictionaries

        I think the first use of the name was for a CDC product in the late 1970's.

        What is the significance of a data dictionary

        It is a place to record some important facts about existing and planned systems. It complements the various diagrams we draw by providing and organizing quantitative and textual information about a system. The only other place where this data is stored is in source code. However, you will find it distributed and duplicated in many different pieces of code. Indeed a significant source of bugs comes for incompatible assumptions made about the data by different pieces of software.

        You need to record information about existing systems so that you can create better systems. You also need to specify the data in your new systems so that the data base can be created and the software written to access it.

        Many media can hold data in an enterprise. You should gather samples, print outs, data base designs, spreadsheets, etc. You need to also record ideas and designs. Thus you need to record data about the data -- so-called metadata.

      1. metadata::jargon="data about data", for example the format of a date.

        A data dictionary describes the data in a system in great detail. The typical format is a data base with forms as an user interface. Some methodologists use the computer science technique of writing BNF (above) to describe data. Some data dictionaries include information on the processes in a system as well as the data stored and flowing through it.

        Data dictionaries are a good place to record physical details like the media and format of data and processes. This may change but you can then avoid putting this detail into the more abstract models: DFDs, Scenarios, ERDs, etc.

        Notice that you can use any number of different CASE and Systems Analysis tools to record this data. Visio, Rational Rose, and Dia can use the Unified Modeling language to create visual data dictionaries. Or you can use simple text files (memos or notes on a PDA) or 3><5 cards and a pencil. Posters are common. It is also easy to generate web pages that store the information -- and then you can link the different pieces of data together.

        Information in a typical data dictionary


          1. Data Elements -- gathered into record structures -- Example: an SSN.
          2. Record Structures -- describe data flows and stores -- Example: your record on SIS+.
          3. Data Flows -- Lists the record structures flowing through the link -- a PAWS report
          4. Data Stores -- Holds Records with the same structure (ideally) -- The Student records in the CMS data base
          5. External Entities -- Sources and Sinks, linked to their data flows -- You!
          6. Processes -- Describe activities, linked to their data flows -- "Print rosters".

          [ERD of data dictonary see list above]

          Notice: all the data in the data dictionary is should be given a date when it was recorded... Ideally the data dictionary should keep a history of changes.

          Data Element


          Table
          ItemExamplePurpose
          LabelSocial Security NumberA carefully selected official name for the data
          AliasSSNList of other names for the data
          TypeNNumeric, alphanumeric, date, string, ...
          Length9How many bytes needed?
          Default ValueNONEif any
          ConstraintsPositive 0-99999999What values are allowed/forbidden
          Syntax999-99-9999Layout of the data on input/output.
          SecurityHuman ResourcesWho have the right to see/change this data
          ...
          Description-+ comments
          Records...Links to record structures where item appears.

          (Close Table)
          We will discuss the design of data elements later( [ d2.html ] ) in this course.

          Record Structure

          A record is a collection of data elements that are always handled together. The idea goes back to the beginning of computer systems when a punched card was a record. The items or fields were pieces of the card. The idea moved onto all other data media and into COBOL. From COBOL to Algol68, Pascal, and C.

          Example of an instance of a record:
          Table
          Field LabelField Data
          SSN123-45-6789
          SId888-88-8888
          NameJoe Coyote
          Address...
          ......

          (Close Table)

          A record is a sequence of named fields -- just like a C/C++ struct or the data members in a C++/Java class. One way of documenting record structures is to draw an UML class diagram.

          UML class called Student with SSN, SId, Name, and Address as attributes

          The simplest technique is to list the field names. But a complete description requires a lot of metadata:
          Table
          Items describing a record
          Label -- name of record type
          Aliases
          Description / Purpose
          Fields
          1. Name of field
          2. Type -- [ Data Element ]
          3. Key? (more later [ d2.html ] )

          Constraints
          Description
          Comments
          Usage -- data flows and data stores

          (Close Table)
          These record structures also map closely into data base descriptions.

          Data Flow

          To document a data flow you need to list the records that flow through it and (optionally) how the flow is (or will be)implemented. A data dictionary should link each data flow to its source and destination.
          Table
          Items describing a Data Flow
          Label
          Aliases
          Records flowing through data flow
          Source
          Destination

          (Close Table)

          Data Store

          Data stores should, ideally, store only a single type of record. You must identify the types of record in a data store. You should indicate if a subset of the items form a key. Each key value determines a unique record. You should note the sequence of the records -- or state that they are in random order.

          You need to note how many there are now and how fast this is growing.
          Table
          Items needed for a Data Store
          Label
          List of Record Structures -- in a normalized system there is only one record type per store.
          Key
          Sequence
          Estimate of size
          Estimate of growth %/year
          Relationships with other data

          (Close Table)

          For example
          Table
          Data Store
          LabelD1: Students
          RecordStudentRecord
          KeyStudentID
          SequenceRandom
          Size10,000
          Growth10% per year
          Related toMajors, Address, EMailId, GPA, ...

          (Close Table)
          A set of inter-related data stores should be documented using an Entity-Relationship Diagram. Ultimately data stores are mapped into data base tables, which will be covered later in the class.

          External Entities


          Table
          External Entity
          Label
          Aliases
          Description / Persona
          List of [ Data Flow ]

          (Close Table)

          Example
          Table
          External Entity
          LabelStudent
          AliasUser
          DescriptionA younger tech-savie person with a need to take the classes in their major
          Data Flowslogin, course_id, course_status, ...

          (Close Table)

          Processes


          Table
          Items describing a Process
          Label
          Aliases
          Description of goals and requirements
          List of connected [ Data Flow ] and [ Data Store ]
          Desirable Qualities (eg secure, reliable, cheap, ...)
          Notes
          Details -- Link to a detailed description (if any). See below

          (Close Table)
          Process descriptions can require special documentation of the detailed logic, see [ r1.html ] and [ r2.html ] for details. However initially you use the techniques in the next section.

        . . . . . . . . . ( end of section Information in a typical data dictionary) <<Contents | End>>

        A simpler formats for Data Dictionaries

        You can document a lot of the information in a data dictionary in the UML. You treat each record type as a class with attributes but no operations. This works OK for small projects when the diagram fits on a page. When it becomes poster sized you may need to go to more complex tools.

        Nearly all the necessary information to define a modern data base can be expressed in a set of simple tables all with the same format. Each table describes the records in a file in the data base and lists the data elements. Here is an example transcribed from Meng-Chun Ling's MS Project "Senior Health Care System" (July 2005)[in the Pfau library]:
        Net

        1. USER::table=following,
          Table
          AttributesDefinitionData Type
          USER_IDUser login IDString
          PasswordUser's PINString

          (Close Table)

        2. REF_PHYSICIAN::table=following,
          Table
          AttributesDefinitionData Type
          LAST_NAMEphysician's last nameString
          FIRST_NAMEfirst nameString
          MID_NAMEmiddle nameString
          SPECIALTYidentifier of specialtyInt

          (Close Table)

        3. REF_PROGRAM::table=following,
          Table
          AttributesDefinitionData Type
          NameName of the programString

          (Close Table)

        4. REF_SOURCE::table=following,
          Table
          AttributesDefinitionData Type
          NameReferral source nameString
          AddressContact addressString
          PhoneContact phoneString

          (Close Table)

        5. REFERRAL::table=following,
          Table
          AttributesDefinitionData Type
          ... 14 attributes

          (Close Table)

        6. ... and so on...

        (End of Net)

      . . . . . . . . . ( end of section Data Dictionaries) <<Contents | End>>

      Describing Processes

        Goals and Stories

          It is often best to define a process in terms of what it aims (or does) achieve. This is the actual goal of the process. This has to be stated in clear, objective, and observable effects. They also need to be short -- just a sentence or two at most. The ideal is a process whose name describes its goal:
           	sort student data
          To be more precise you specify the resources needed by the process and the results that it achieves.
           Given randomly ordered student records sort them by student I.D.

          Another approach is to write a Story describing what the process does. A story is a simple paragraph describing what we want the process to do.

          It is wise to record the goal of a process -- since this can get lost as the system develops.

        Functions and Formulas

          In many businesses part of the know-how is in the form of mathematical formulas. Many domains have standard calculations. Once you have a Process that can be expressed this way then it is a bad mistake to draw a DFD of it. Instead you need to define meaning of the process as a formula.

          Expressing formula in text is easily done by using expressions from a programming language. Or you can use the Equation Writer that is available with most word processors. There is a standard Computer Science language called ΤΕΧ (pronounce "teck) that is designed for writing mathematics.

          There have been experiments in using more complex logic and discrete math to describe the behavior of a system in abstract form and then execute the model to see if the ideas work. It is also possible to manipulate them to search out unwanted behaviors. These are a part of [ ../cs556/ ] Formal Methods. So they are not covered here.

        Scenarios

          Introduction

          A scenario provides more details on a complex process. It is a numbered sequence of simple actions by subjects that achieves some stated goal. A scenario must mention who does each action (the subjects).

          To completely describe a complex process it easiest to write many simple scenarios. Each is a simple slice through the possibilities. To handle algorithms and complex decisions you need more complicated techniques mentioned below.

          A scenario can be used to describe any pattern of activity that you either (1) observe in the real world, (2) imagine as existing, or (3) plan to be part of your future system. A Scenario can be Physical (mentioning the technology) or Essential (abstracted from the technology). Essential Scenarios are Logical Models.

          Scenarios are easy to understand -- People get "scenarios". Both by users and technologists. They map simply into specifications and designs for programs. They are also a very good Modeling tool to use with non-computer people.

        1. Scenario::Wikipedia= See http://en.wikipedia.org/wiki/Scenario_(computing).

          Principles -- Keep Scenarios Essential

          As far as possible avoid mentioning technology in your scenarios. There was a time when scenarios tended to include statements like "put a new stack of cards in the hopper". These days lots of scenarios tend to refer to web-specific actions:
           		Jo clicks the Red Cancel Link.
          Try to avoid this if you can find an alternative. For example
           		Jo selects "Cancel"
          or
           		Jo cancels ....
          Bear in mind that a significant number of users are not using a mouse and so can not "click" anything!

          Principle -- Keep Scenarios Simple

          Notice: a scenario has no branches, conditions, exceptions, extensions, or parallelism. It should be strictly sequential.

          Principle -- Use Simple Language in Scenarios

          You should also use simple language in scenarios and borrow from the stakeholder's words and phrases.

          Principle -- There is more to life than scenarios

          A Scenario can not express all the things that happen in real systems. It is a slice of life, It shows one simple path through the possibilities. Real systems make decisions and follow different branches at different times. A single scenario can not describe all the possibilities. Neither can it handle parallelism -- when many things are happening and can happen in different orders. For example: three people are working on a common set of tasks -- stuffing, stamping, and sorting envelopes for example. There is no one scenario which describes all the possible sequences of events.

          To express more complex processes an activity diagram [ r1.html#Activity%20Diagrams ] can be used. I would make each "activity box" stand for a simple scenario. You can handle non-determinism of parallel system by drawing a Data Flow Diagram [ a4.html ] (Previously).

          Quotations from Scott Ambler

            See SD's Agile Modeling Newsletter January 2006 , Modeling the Real-World: Usage Scenarios By Scott W. Ambler

            The Nitty-Gritty: A Detailed Example
            Here's a scenario outlining a successful withdrawal attempt at an automated teller machine (ATM).
            1. John Smith presses the "Withdraw Funds" button.
            2. The ATM displays the preset withdrawal amounts ($20, $40, ...).
            3. John chooses the option to specify the amount of the withdrawal.
            4. The ATM displays an input field for the withdrawal amount.
            5. John indicates that he wishes to withdraw $50 dollars.
            6. The ATM displays a list of John's accounts: a checking and two savings accounts.
            7. John chooses his checking account.
            8. The ATM verifies that the amount may be withdrawn from his account.
            9. The ATM verifies that there is at least $50 available to be disbursed from the machine.
            10. The ATM debits John's account by $50.
            11. The ATM disburses $50 in cash.
            12. The ATM displays the "Do you wish to print a receipt" options.
            13. John indicates "Yes".
            14. The ATM prints the receipt.

            The Sky View: A High-Level Example
            Now let's define a wider range of ATM functionality:
            1. Sally Jones places her bank card into the ATM.
            2. Sally successfully logs into the ATM using her personal identification number.
            3. Sally deposits her weekly paycheck of $350 into her savings account.
            4. Sally pays her phone bill of $75, her electric bill of $145, her cable bill of $55, and her water bill of $85 from her savings account.
            5. Sally attempts to withdraw $100 from her savings account for the weekend, but discovers that she has insufficient funds.
            6. Sally withdraws $40 and gets her card back.

            [...]

            Use cases vs Scenarios
            As you can imagine, there are several differences between use cases and scenarios. First, a use case typically refers to generic actors, such as Customer, while scenarios typically refer to specific actors such as John Smith and Sally Jones. You could write a generic scenario, but it's usually better to personalize it to increase understandability. Second, usage scenarios describe a single path of logic, whereas use cases typically describe several paths (the basic course, plus any appropriate alternate paths). [...]

          . . . . . . . . . ( end of section Quotations from Scott Ambler) <<Contents | End>>

          We will discuss use cases later in this course and study them in detail in CSCI375.

        . . . . . . . . . ( end of section Scenarios) <<Contents | End>>

        Prototypes

          "Never talk to a user without a prototype in your hand".

          What is a prototype


          Table
          A prototype A prototype
          is a Physical Modelis not a Logical Model
          can be an executable programis not useful to the users
          can have a data baseis not full of all the real data
          demonstrates how something could workis not how it will be coded
          demonstrates some behaviorsis not a finished product
          works on some inputis not completely correct
          is produced quicklyis not a high quality long term project
          runsis not efficient on real data
          tests ideasis not a test of a prgram
          is for the useris not always executable code
          provokes discussionis not the best possible solution to the problem
          can be used to sell a projectis not a promise that a project is feasible

          (Close Table)

          Definition -- Prototypes are not Iterations

          An iteration is a high quality, usable subset of a project. It meets some of the requirements and is designed so that it is easy to add more requirements. Each iteration modifies the existing system. Each iteration is well made and documented but doesn't meet all requirements, yet.

          A prototype is often incomplete and nearly always of low quality. It is produced quickly using specialized tools. The result is not run quickly. It is not designed so that it can be easily reused in the project.

          There is a trap with trying to change a prototype into an iteration: it is harder to put quality into bad software than it is to add features to good software.

          Types of Prototypes

            It helps to study prototypes used outside the software field. The following all have analogies in software and systems development.
            Story Boards
            A poster showing the successive screens/pages as a scenario is executed. The term comes form the movie industry where directors use them. You will often find images of story boards as "extras" on DVDs. These are not high-tech prototypes. Use a board and stick cards on it. Use story boards as a basis of planning and discussion.
            Mock Ups
            A mock up looks like the real thing and may even do some things but is not properly constructed. It's possible the term comes from publishers. A computer example is faking a web page to show its look-and-feel.

            Concept Cars
            Many automobile companies produce strange and wonderful one off cars in exhibitions to show what is possible. Most don't make it into production. Use concept cars to sell a new system to management?
            Bread Boards
            Once upon a time electronic engineers would try out ideas on the kitchen table and use a bread board and pins to hold the wires in place. To this day you can buy special electronic breadboards. Typically they become an untidy mess of wires with many inputs, meters, and probes attached to it. Used by software people to try out an algorithm -- full of extra outputs and with a user interface designed for techies. Don't let the user see these!

            A variation on breadboard prototyping is for complex systems which may have unexpected performance problems and timing hazards. Here the protoype is for a component. It provides all the necessary interfaces and even responds similarly to the real component... however it doesn't compute the results correctly. This is a recent proposal [Montagne10] that needs thinking about.

            Scale Models
            A scale model has all the functionality of the final system but does not have the full data base. It's purpose is to spot logic problems quickly and to test if the design will scale up.

          . . . . . . . . . ( end of section Types of Prototypes) <<Contents | End>>

        . . . . . . . . . ( end of section Prototypes) <<Contents | End>>

      . . . . . . . . . ( end of section Describing Processes) <<Contents | End>>

      Review Questions

      1. Give an example from your experience of a solution that has become a problem.
      2. Write a basic scenario for one of the following. What happens when you
        1. Pump gas.
        2. Register for a class.
        3. Submit CS372 assigned work.
        4. Plan to interview someone.
        5. Write a program.
        6. Try to solve a problem.

      3. Describe and distinguish: glossary, BNF, and data dictionary.
      4. What is the purpose of a data dictionary.
      5. Compare and contrast briefly: item vs record, record vs file, file vs data base.
      6. What information should a data dictionary record about
        1. a data element
        2. a data record
        3. a data store
        4. a data flow
        5. a process

      7. What fields would you expect to find in the following records:
        1. Name of a person
        2. Address of person or location
        3. A Phone number in the USA

      8. List the types of prototype.

      . . . . . . . . . ( end of section Review Questions) <<Contents | End>>

      Online Enrichment

        Example Glossaries Online

        I have several dozen [ ../samples/ ] glossaries written in my own style -- XBNF.

        Example of a First Data Dictionary

        This is an expanded glossary [ dd01.html ] for a project carried out by one of my graduate students.

        Prototypes Online

        [ search;_ylt=A0oGdOIIk7xM..AAEkMPxQt.?ei=UTF-8&q=Bread+board&fr=alltheweb&p=electronic+breadboard&rs=0&fr2=rs-top ] [ images?_adv_prop=image&fr=alltheweb&va=storyboard ] [ images;_ylt=A2KJkexur7xMQjEAlG.JzbkF?p=Mock%2Bup&y=Search&fr=alltheweb&ei=utf-8&js=1&x=wrt ] [ images?hl=en&expIds=25657,26637,27102,27143&sugexp=ldymls&xhr=t&q=concept+cars&cp=10&wrapid=tljp1287435194937020&um=1&ie=UTF-8&source=univ&ei=vbO8TMaQDYT6swPfvunmDg&sa=X&oi=image_result_group&ct=title&resnum=1&sqi=2&ved=0CDAQsAQwAA ]

        BNF on the Wikipedia

        [ Backus-Naur_Form ]

        Tom DeMarco

      1. DeMarco::person=inventor of a kind of DFD and data dictionary, [ Tom_DeMarco] .

    . . . . . . . . . ( end of section System Modeling II) <<Contents | End>>

    Abbreviations

  1. TBA::="To Be Announced".
  2. TBD::="To Be Done".

    Links

    Notes -- Analysis [ a1.html ] [ a2.html ] [ a3.html ] [ a4.html ] [ a5.html ] -- Choices [ c1.html ] [ c2.html ] [ c3.html ] -- Data [ d1.html ] [ d2.html ] [ d3.html ] [ d4.html ] -- Rules [ r1.html ] [ r2.html ] [ r3.html ]

    Projects [ project1.html ] [ project2.html ] [ project3.html ] [ project4.html ] [ project5.html ] [ projects.html ]

    Field Trips [ F1.html ] [ F2.html ] [ F3.html ]

    [ about.html ] [ index.html ] [ schedule.html ] [ syllabus.html ] [ readings.html ] [ review.html ] [ glossary.html ] [ contact.html ] [ grading/ ]

End