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Thinking in C++
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This chapter attempts to give you a feel for the broad issues of object-oriented programming and C++, including why OOP is different, and why C++ in particular is different, concepts of OOP methodologies, and finally the kinds of issues you will encounter when moving your own company to OOP and C++.

OOP and C++ may not be for everyone. It’s important to evaluate your own needs and decide whether C++ will optimally satisfy those needs, or if you might be better off with another programming system (including the one you’re currently using). If you know that your needs will be very specialized for the foreseeable future and if you have specific constraints that may not be satisfied by C++, then you owe it to yourself to investigate the alternatives[24]. Even if you eventually choose C++ as your language, you’ll at least understand what the options were and have a clear vision of why you took that direction.

You know what a procedural program looks like: data definitions and function calls. To find the meaning of such a program you have to work a little, looking through the function calls and low-level concepts to create a model in your mind. This is the reason we need intermediate representations when designing procedural programs – by themselves, these programs tend to be confusing because the terms of expression are oriented more toward the computer than to the problem you’re solving.

Because C++ adds many new concepts to the C language, your natural assumption may be that the main( ) in a C++ program will be far more complicated than for the equivalent C program. Here, you’ll be pleasantly surprised: A well-written C++ program is generally far simpler and much easier to understand than the equivalent C program. What you’ll see are the definitions of the objects that represent concepts in your problem space (rather than the issues of the computer representation) and messages sent to those objects to represent the activities in that space. One of the delights of object-oriented programming is that, with a well-designed program, it’s easy to understand the code by reading it. Usually there’s a lot less code, as well, because many of your problems will be solved by reusing existing library code.

2: Making & Using Objects

[4] See Multiparadigm Programming in Leda by Timothy Budd (Addison-Wesley 1995).

[5] You can find an interesting implementation of this problem in Volume 2 of this book, available at

[6] Some people make a distinction, stating that type determines the interface while class is a particular implementation of that interface.

[7] I’m indebted to my friend Scott Meyers for this term.

[8] This is usually enough detail for most diagrams, and you don’t need to get specific about whether you’re using aggregation or composition.

[9] An excellent example of this is UML Distilled, by Martin Fowler (Addison-Wesley 2000), which reduces the sometimes-overwhelming UML process to a manageable subset.

[10] My rule of thumb for estimating such projects: If there’s more than one wild card, don’t even try to plan how long it’s going to take or how much it will cost until you’ve created a working prototype. There are too many degrees of freedom.

[11] Thanks for help from James H Jarrett.

[12] More information on use cases can be found in Applying Use Cases by Schneider & Winters (Addison-Wesley 1998) and Use Case Driven Object Modeling with UML by Rosenberg (Addison-Wesley 1999).

[13] My personal take on this has changed lately. Doubling and adding 10 percent will give you a reasonably accurate estimate (assuming there are not too many wild-card factors), but you still have to work quite diligently to finish in that time. If you want time to really make it elegant and to enjoy yourself in the process, the correct multiplier is more like three or four times, I believe.

[14] For starters, I recommend the aforementioned UML Distilled.

[15] Python ( is often used as “executable pseudocode.”

[16] At least one aspect of evolution is covered in Martin Fowler’s book Refactoring: improving the design of existing code (Addison-Wesley 1999). Be forewarned that this book uses Java examples exclusively.

[17] This term is explored in the Design Patterns chapter in Volume 2.

[18] This is something like “rapid prototyping,” where you were supposed to build a quick-and-dirty version so that you could learn about the system, and then throw away your prototype and build it right. The trouble with rapid prototyping is that people didn’t throw away the prototype, but instead built upon it. Combined with the lack of structure in procedural programming, this often produced messy systems that were expensive to maintain.

[19] Although this may be a more American perspective, the stories of Hollywood reach everywhere.

[20] Including (especially) the PA system. I once worked in a company that insisted on broadcasting every phone call that arrived for every executive, and it constantly interrupted our productivity (but the managers couldn’t begin to conceive of stifling such an important service as the PA). Finally, when no one was looking I started snipping speaker wires.

[21] I say “may” because, due to the complexity of C++, it might actually be cheaper to move to Java. But the decision of which language to choose has many factors, and in this book I’ll assume that you’ve chosen C++.

[22] However, look at Dan Saks’ columns in the C/C++ User’s Journal for some important investigations into C++ library performance.

[23] Because of its productivity improvements, the Java language should also be considered here.

[24] In particular, I recommend looking at Java ( and Python (

Thinking in C++
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   Reproduced courtesy of Bruce Eckel, MindView, Inc. Design by Interspire