However, please remember that one of the ideals for code is simplicity, rather than complexity. You don’t demonstrate your cleverness by writing the most complex program. Rather, you demonstrate competence by writing the simplest code that does the job.

If you work by yourself, don’t expect to spend less time than that (maybe 15 hours a week for 14 weeks).

That’s quite an ambitious goal because much modern software relies on techniques considered advanced just a few years ago.

Consequently, I chose the topics for this book to cover what is needed to get started with real-world programming, not just what is easy to teach and learn.

If you need a technique to get basic work done right, I describe it, demonstrate concepts and language facilities needed to support the technique, provide exercises for it, and expect you to work on those exercises.

This book is aimed to serve those who do want to write or understand nontrivial programs.

working with friends forces you to articulate your ideas, which is just about the most efficient way of testing your understanding and making sure you remember.

Remember: programming is (among other things) a practical skill that you need to practice to master. If you don’t write code (do several exercises for each chapter), reading this book will be a pointless theoretical exercise

And please don’t be too impatient. Learning any major new and valuable skill takes time and is worth it.

for helping me hide away from distractions to get writing done during the summers.

Each chapter is a reasonably self-contained unit, meant to be read in “one sitting” (logically, if not always feasible on a student’s tight schedule)

Learning to ask the right (often hard) questions is an essential part of learning to think as a programmer.

but please don’t underestimate a simple statement like “This is often useful.” If we quietly emphasize that something is important, we mean that you’ll sooner or later waste days if you don’t master it.

If you haven’t done the drills, you have not “done” the book.

characterize our approach as “depth-first.” It is also “concrete-first” and “concept-based.” First, we quickly (well, relatively quickly, Chapters 1–11) assemble a set of skills needed for writing small practical programs. In doing so, we present a lot of tools and techniques in minimal detail.

We focus on simple concrete code examples because people grasp the concrete faster than the abstract.

You must run before you can walk!

Nobody should be satisfied with “because that’s the way it is” as an explanation.

More importantly, an understanding of ideals and reasons allows you to generalize from what you know to new situations and to combine ideas and tools in novel ways to address new problems. Knowing “why” is an essential part of acquiring programming skills.

Successful commercial programs with hundreds of thousands of lines of code are based on techniques that we illustrate in a dozen 50-line programs.

The fastest way to understand real-world code is through a good understanding of the fundamentals.

More generally, a textbook or a course must lead students through a series of subsets. We consider it our responsibility to select topics and to provide emphasis. We can’t just present everything, so we must choose; what we leave out is at least as important as what we leave in — at each stage of the journey

only through complete understanding of a problem and its solution can you express a correct program for it, and only through constructing and testing a program can you be certain that your understanding is complete.

Please don’t underestimate the importance of the notion of type safety. Types are at the center of most notions of correct programs, and some of the most effective techniques for constructing programs rely on the design and use of types

o deal with input, a program usually contains some data, sometimes referred to as its data structures or its state. For example, a calendar program may contain lists of holidays in various countries and a list of your appointments.

Most of the rest of this book could be seen as discussing that last category: how do we express a program as a set of cooperating parts and how can they share and exchange data? These are key questions in programming.

Inputs to a part of a program are often called arguments and outputs from a part of a program are often called results.

Our job as programmers is to express computations • Correctly • Simply • Efficiently

Please note the order of those ideals: it doesn’t matter how fast a program is if it gives the wrong results. Similarly, a correct and efficient program can be so complicated that it must be thrown away or completely rewritten to produce a new version (release).

Remember, useful programs will always be modified to accommodate new needs, new hardware, etc. Therefore a program — and any part of a program — should be as simple as possible to perform its task.

we must concern ourselves with the structure of code. Paradoxically, concerns for structure and “quality of code” are often the fastest ways of getting something to work. When programming is done well, such concerns minimize the need for the most frustrating part of programming: debugging;

Our main tool for organizing a program — and for organizing our thoughts as we program — is to break up a big computation into many little ones. This technique comes in two variations: - Abstraction - Divide and Conquer

Without structure, you are (metaphorically speaking) building with mud bricks. It can be done, but you’ll never get to the fifth floor (mud bricks lack the structural strength for that).

The most basic building block of programs is an expression. An expression computes a value from a number of operands. The simplest expression is simply a literal value, such as 10, 'a', 3.14, or "Norah". Names of variables are also expressions. A variable represents the object of which it is the name.

Sometimes (as an lvalue) length refers to the box (object) and sometimes (as an rvalue) length refers to the value in that box.

C++ offers the notion of a symbolic constant, that is, a named object to which you can’t give a new value after it has been initialized. For example:

Consequently, we prefer not to use literals (except very obvious ones, such as 0 and 1) in most places in our code. Instead, we use constants with descriptive names. Non-obvious literals in code (outside definitions of symbolic constants) are derisively referred to as magic constants.

And by the way, 299792458 is one of the fundamental constants of the universe: the speed of light in vacuum measured in meters per second. If you didn’t instantly recognize that, why would you expect not to be confused and slowed down by other literals embedded in code? Avoid magic constants!

To handle cases where the value of a “variable” that is initialized with a value that is not known at compile time but never changes after initialization, C++ offers a second form of constant (a const):

Note that a<b<c means (a<b)<c and that a<b evaluates to a Boolean value: true or false. So, a<b<c will be equivalent to either true<c or false<c. In particular, a<b<c does not mean “Is b between a and c?” as many have naively (and not unreasonably) assumed. Thus, a<b<c is basically a useless expression.

The rule (for the types we have presented so far) is that if an operator has an operand of type double, we use floating-point arithmetic yielding a double result; otherwise, we use integer arithmetic yielding an int result. For example:

C++11 introduced an initialization notation that outlaws narrowing conversions. For example, we could (and should) rewrite the troublesome examples above using a {}-list notation, rather than the = notation:

The {}-list-based notation is known as universal and uniform initialization

cout<< "Please enter a length followed by a unit (c or i):\n"; cin >> length >> unit;

a selection based on comparison of a value against several constants. Such selection is so common that C++ provides a special statement for it: the switch-statement.