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Finishing the System`s Outlines

This second part of a two-part article completes our coverage of how to talk to a client so that you are both on the same page when designing a system and understanding what it will be required to do. It is excerpted from Prefactoring, written by Ken Pugh (O'Reilly; ISBN: 596008740). Copyright 2007 O'Reilly Media, Inc. All rights reserved. Used with permission from the publisher. Available from booksellers or direct from O'Reilly Media.

TABLE OF CONTENTS:
  1. Finishing the System`s Outlines
  2. Abstracting
  3. Not Just a String
  4. Constant Avoidance
  5. Prototypes Are Worth a Thousand Words
By: O'Reilly Media
Rating: starstarstarstarstar / 2
May 22, 2008

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Clumping

When Sam described his customers in detail, he mentioned that he needed to keep track of each customer's home address, including street, city, state, and Zip Code, as well as credit card billing address, including street, city, state, and Zip Code.

I asked him, "Do both of those addresses contain the same information?"

He replied affirmatively.

I said, "Then let's just describe the combination as an Address. That way, you don't have to keep mentioning all the parts unless there is something different about them."

"OK," he answered.

We clumped the data into a class, as follows:

  class Address
      {
      String line1;
      String line2;
      String city;
      String state;
      String zip;
      }

At this point, we simply clump the related data, even though we have not assigned any behavior to the class. This data object helps in abstraction and in cutting down parameter lists. Even though the class contains only data at this point, we might be able to assign responsibility to it later on.*

Clumping and lumping look similar, but they have distinctly different meanings. Clumping involves combining a set of attributes into a single named concept. The attributes should form a cohesive whole. Lumping involves using a single name for two different concepts. Clumping is an abstraction technique, which makes for an efficient description of a set of data. Lumping can hide relevant distinctions between concepts.

CLUMP DATA SO THAT THERE IS LESS TO THINK ABOUT

Clumping data cuts down on the number of concepts that have to be kept in mind.



 
 
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