MaximumValueUntilOverflowa better name than
maxval? I don't think so.
What follows is a set of short essays that collectively encourage a philosophy of clarity in programming rather than giving hard rules. I don't expect you to agree with all of them, because they are opinion and opinions change with the times. But they've been accumulating in my head, if not on paper until now, for a long time, and are based on a lot of experience, so I hope they help you understand how to plan the details of a program. (I've yet to see a good essay on how to plan the whole thing, but then that's partly what this course is about.) If you find them idiosyncratic, fine; if you disagree with them, fine; but if they make you think about why you disagree, that's better. Under no circumstances should you program the way I say to because I say to; program the way you think expresses best what you're trying to accomplish in the program. And do so consistently and ruthlessly.
Your comments are welcome.
Typographic conventions consistently held are important to clear presentation, of course - indentation is probably the best known and most useful example - but when the ink obscures the intent, typography has taken over. So even if you stick with plain old typewriterlike output, be conscious of typographic silliness. Avoid decoration; for instance, keep comments brief and banner-free. Say what you want to say in the program, neatly and consistently. Then move on.
maxphysaddrsay. An array index used on every line of a loop needn't be named any more elaborately than
elementnumberis more to type (or calls upon your text editor) and obscures the details of the computation. When the variable names are huge, it's harder to see what's going on. This is partly a typographic issue; consider
for(i=0 to 100) array[i]=0vs.
for(elementnumber=0 to 100) array[elementnumber]=0;The problem gets worse fast with real examples. Indices are just notation, so treat them as such.
Pointers also require sensible notation.
np is just as
nodepointer if you consistently use a naming
convention from which
np means ``node pointer'' is easily
derived. More on this in the next essay.
As in all other aspects of readable programming,
consistency is important in naming. If you call one
maxphysaddr, don't call its cousin
Finally, I prefer minimumlength but
maximuminformation names, and then let the context fill in the
rest. Globals, for instance, typically have little context
when they are used, so their names need to be relatively
evocative. Thus I say
MaximumPhysicalAddress) for a global variable, but
NodePointer for a pointer locally defined and used. This
is largely a matter of taste, but taste is relevant to clarity.
I eschew embedded capital letters in names; to my proseoriented eyes, they are too awkward to read comfortably. They jangle like bad typography.
Consider: When you have a pointer to an object, it is a name for exactly that object and no other. That sounds trivial, but look at the following two expressions:
np node[i]The first points to a node, the second evaluates to (say) the same node. But the second form is an expression; it is not so simple. To interpret it, we must know what
iis, and that
nodeare related by the (probably unspecified) rules of the surrounding program. Nothing about the expression in isolation can show that
iis a valid index of
node, let alone the index of the element we want. If
kare all indices into the node array, it's very easy to slip up, and the compiler cannot help. It's particularly easy to make mistakes when passing things to subroutines: a pointer is a single thing; an array and an index must be believed to belong together in the receiving subroutine.
An expression that evaluates to an object is inherently more subtle and errorprone than the address of that object. Correct use of pointers can simplify code:
lp->type.If we want the next element's type, it's
iadvances but the rest of the expression must stay constant; with pointers, there's only one thing to advance.
Typographic considerations enter here, too. Stepping through structures using pointers can be much easier to read than with expressions: less ink is needed and less effort is expended by the compiler and computer. A related issue is that the type of the pointer affects how it can be used correctly, which allows some helpful compiletime error checking that array indices cannot share. Also, if the objects are structures, their tag fields are reminders of their type, so
np->leftis sufficiently evocative; if an array is being indexed the array will have some wellchosen name and the expression will end up longer:
node[i].left.Again, the extra characters become more irritating as the examples become larger.
As a rule, if you find code containing many similar, complex expressions that evaluate to elements of a data structure, judicious use of pointers can clear things up. Consider what
if(goleft) p->left=p->right->left; else p->right=p->left->right;would look like using a compound expression for
p. Sometimes it's worth a temporary variable (here
p) or a macro to distill the calculation.
if's, so they need to read appropriately.
if(checksize(x))is unhelpful because we can't deduce whether checksize returns true on error or non-error; instead
if(validsize(x))makes the point clear and makes a future mistake in using the routine less likely.
But I do comment sometimes. Almost exclusively, I use them as an introduction to what follows. Examples: explaining the use of global variables and types (the one thing I always comment in large programs); as an introduction to an unusual or critical procedure; or to mark off sections of a large computation.
There is a famously bad comment style:
i=i+1; /* Add one to i */and there are worse ways to do it:
/********************************** * * * Add one to i * * * **********************************/ i=i+1;Don't laugh now, wait until you see it in real life.
Avoid cute typography in comments, avoid big blocks of comments except perhaps before vital sections like the declaration of the central data structure (comments on data are usually much more helpful than on algorithms); basically, avoid comments. If your code needs a comment to be understood, it would be better to rewrite it so it's easier to understand. Which brings us to
Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.
Rule 2. Measure. Don't tune for speed until you've measured, and even then don't unless one part of the code overwhelms the rest.
Rule 3. Fancy algorithms are slow when n is small, and n is usually small. Fancy algorithms have big constants. Until you know that n is frequently going to be big, don't get fancy. (Even if n does get big, use Rule 2 first.) For example, binary trees are always faster than splay trees for workaday problems.
Rule 4. Fancy algorithms are buggier than simple ones, and they're much harder to implement. Use simple algorithms as well as simple data structures.
The following data structures are a complete list for almost all practical programs:
Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be selfevident. Data structures, not algorithms, are central to programming. (See Brooks p. 102.)
Rule 6. There is no Rule 6.
ifstatements. The reason is that the complexity of the job at hand, if it is due to a combination of independent details, can be encoded. A classic example of this is parsing tables, which encode the grammar of a programming language in a form interpretable by a fixed, fairly simple piece of code. Finite state machines are particularly amenable to this form of attack, but almost any program that involves the `parsing' of some abstract sort of input into a sequence of some independent `actions' can be constructed profitably as a datadriven algorithm.
Perhaps the most intriguing aspect of this kind of design is that the tables can sometimes be generated by another program - a parser generator, in the classical case. As a more earthy example, if an operating system is driven by a set of tables that connect I/O requests to the appropriate device drivers, the system may be `configured' by a program that reads a description of the particular devices connected to the machine in question and prints the corresponding tables.
One of the reasons data-driven programs are not common, at least among beginners, is the tyranny of Pascal. Pascal, like its creator, believes firmly in the separation of code and data. It therefore (at least in its original form) has no ability to create initialized data. This flies in the face of the theories of Turing and von Neumann, which define the basic principles of the stored-program computer. Code and data are the same, or at least they can be. How else can you explain how a compiler works? (Functional languages have a similar problem with I/O.)
Some of the complexity is passed to the routine pointed to. The routine must obey some standard protocol - it's one of a set of routines invoked identically - but beyond that, what it does is its business alone. The complexity is distributed.
There is this idea of a protocol, in that all functions used similarly must behave similarly. This makes for easy documentation, testing, growth and even making the program run distributed over a network - the protocol can be encoded as remote procedure calls.
I argue that clear use of function pointers is the heart of objectoriented programming. Given a set of operations you want to perform on data, and a set of data types you want to respond to those operations, the easiest way to put the program together is with a group of function pointers for each type. This, in a nutshell, defines class and method. The OO languages give you more of course - prettier syntax, derived types and so on - but conceptually they provide little extra.
Combining data-driven programs with function pointers leads to an astonishingly expressive way of working, a way that, in my experience, has often led to pleasant surprises. Even without a special OO language, you can get 90% of the benefit for no extra work and be more in control of the result. I cannot recommend an implementation style more highly. All the programs I have organized this way have survived comfortably after much development - far better than with less disciplined approaches. Maybe that's it: the discipline it forces pays off handsomely in the long run.
/usr/include/sysstuff is terrible this way.
There's a little dance
#ifdef's that can prevent a file being read twice,
but it's usually done wrong
in practice - the
#ifdef's are in the file itself, not the
file that includes it. The result is often thousands of
needless lines of code passing through the lexical analyzer,
which is (in good compilers) the most expensive phase.
Just follow the simple rule.