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Does Listing 36-1 really need any more documenting than this If such additional documentation existed, would you read it Would you need to read it The minimalist approach treats code as though it were meat and comments as fat: You should include only enough fat to make the meat palatable; any more is unhealthy. Following are other cooking analogies that are also applicable: A good chef doesn t rely on fat alone to create flavor; he makes all his ingredients do as much double duty as possible. He uses the solidified meat drippings sticking to the bottom of his roasting pan, for example, into a finished sauce by simply adding stock and a little fat. In plain English, your code should document itself as much as possible by using obvious variable and object names, regardless of how long they become. If your code reads as Listing 36-1 does, it needs only a little bit of commenting (fat) to bring everything together.

Publisher:Addison Wesley As we saw in Section 771 (p 260), member functions have an extra implicit parameter that is Professional a pointer to an object of the class type This implicit parameter is named this, and is bound to Pub the member function is the object on which Date: February 14, 2005 called Member functions may not define the this Print ISBN: 0-201-72148-1 parameter; the compiler does so implicitly The body of a member function may explicitly use thethis pointer, Pages: 912 required to do so The compiler treats an unqualified reference to a but is not class Contents | as if Table ofmember Index it had been made through the this pointer

System Analysis, Design, and Development, by Charles S. Wasson Copyright 2006 by John Wiley & Sons, Inc.

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The greater the chef, the more assistance he needs to create his masterpieces Do you really think that the world s greatest chef peels and juliennes his own carrots, or does he have a team of assistant chefs do these things for him so that he can concentrate on more important matters You re probably using only a small percentage of your IDE s (Integrated Development Environment) capabilities, so learn and implement every trick and technique that makes sense 41 shows you how to effectively use combinations of useful CF Studio productivity tools that you ve probably never used before to make your code faster to write and easier to document Use something else to do the dirty work for you.

When to Use the this Pointer been completely updated, reorganized, and rewritten to help This popular tutorial introduction to standard C++ has

programmers learn the language faster and use it in a more modern, effective wayJust as C++ has evolved since the last edition, so it the authors' unnecessary to refer now introduce this standard member function, giving Althoughhasis usually approach to teaching it Theyexplicitly tothe C++inside alibrary from the beginning,there is readers the means to write must do so: when we need master every the object as a whole rather than to one case in which weuseful programs without first having to to refer to language detail Highlighting today's best practices, they show object The most common case where we must use this is performance a member of thehow to write programs that are safe, can be built quickly, and yet offer outstandingin functions that Examples reference to the the library, and explain the features invoked return a that take advantage of object on which they were of C++, also show how to make the best use of the language As in its previous editions, the book's authoritative discussion of fundamental C++ concepts and techniques makes

Circular Error Probability (CEP) The Gaussian probability density function (normal distribution) referenced to a central point with concentric rings representing the standard deviations of data dispersion. Cumulative Error A measure of the total cumulative errors inherent within and created by a system or product when processing statistically variant inputs to produce a standard output or outcome. Logarithmic Distribution (Lognormal) An asymmetrical, graphical plot of the Poisson probability density function depicting the dispersion and frequency of independent data occurrences about a mean that is skewed from a median of the data distribution. Normal Distribution A graphical plot of the Gaussian probability density function depicting the symmetrical dispersion and frequency of independent data occurrences about a central mean. Variance (Statistical) A measure of the degree of spread among a set of values; a measure of the tendency of individual values to vary from the mean value. It is computed by subtracting the mean value from each value, squaring each of these differences, summing these results, and dividing this sum by the number of values in order to obtain the arithmetic mean of these squares. (Source: DSMC T&E Mgt. Guide, DoD Glossary of Test Terminology, p. B-21)

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