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  • Copyright 1999
  • Dimensions: 7-3/8x9-1/4
  • Pages: 288
  • Edition: 1st
  • Book
  • ISBN-10: 0-201-61586-X
  • ISBN-13: 978-0-201-61586-9

With the same insight and authority that made their book The Unix Programming Environment a classic, Brian Kernighan and Rob Pike have written The Practice of Programming to help make individual programmers more effective and productive.

The practice of programming is more than just writing code. Programmers must also assess tradeoffs, choose among design alternatives, debug and test, improve performance, and maintain software written by themselves and others. At the same time, they must be concerned with issues like compatibility, robustness, and reliability, while meeting specifications.

The Practice of Programming covers all these topics, and more. This book is full of practical advice and real-world examples in C, C++, Java, and a variety of special-purpose languages. It includes chapters on:

  • debugging: finding bugs quickly and methodically
  • testing: guaranteeing that software works correctly and reliably
  • performance: making programs faster and more compact
  • portability: ensuring that programs run everywhere without change
  • design: balancing goals and constraints to decide which algorithms and data structures are best
  • interfaces: using abstraction and information hiding to control the interactions between components
  • style: writing code that works well and is a pleasure to read
  • notation: choosing languages and tools that let the machine do more of the work

Kernighan and Pike have distilled years of experience writing programs, teaching, and working with other programmers to create this book. Anyone who writes software will profit from the principles and guidance in The Practice of Programming.

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The Practice of Programming

Chapter 1

Good Clues, Easy Bugs

Oops! Something is badly wrong. My program crashed, or printed nonsense, or seems to be running forever. Now what?

Beginners have a tendency to blame the compiler, the library, or anything other than their own code. Experienced programmers would love to do the same, but they know that, realistically, most problems are their own fault.

Fortunately, most bugs are simple and can be found with simple techniques. Examine the evidence in the erroneous output and try to infer how it could have been produced. Look at any debugging output before the crash; if possible get a stack trace from a debugger. Now you know something of what happened, and where. Pause to reflect. How could that happen? Reason back from the state of the crashed program to determine what could have caused this.

Debugging involves backwards reasoning, like solving murder mysteries. Something impossible occurred, and the only solid information is that it really did occur. So we must think backwards from the result to discover the reasons. Once we have a full explanation, we'll know what to fix and, along the way, likely discover a few other things we hadn't expected.

Look for familiar patterns.

...

Examine the most recent change.

...

Don't make the same mistake twice.

...

Debug it now, not later.

Being in too much of a hurry can hurt. Don't ignore a crash when it happens; track it down right away, since it may not happen again until it's too late. A famous example occurred on the Mars Pathfinder mission. After the flawless landing in July 1997 the spacecraft's computers tended to reset once a day or so, and the engineers were baffled. Once they tracked down the problem, they realized that they had seen that problem before. During pre-launch tests the resets had occurred, but had been ignored because the engineers were working on unrelated problems. So they were forced to deal with the problem later when the machine was tens of millions of miles away and much harder to fix.

Get a stack trace.

...

Read before typing.

...

Explain your code to someone else.

Another effective technique is to explain your code to someone else. This will often cause you to explain the bug to yourself. Sometimes it takes no more than a few sentences, followed by an embarrassed ''Never mind, I see what's wrong. Sorry to bother you.'' This works remarkably well; you can even use non-programmers as listeners. One university computer center kept a teddy bear near the help desk. Students with mysterious bugs were required to explain them to the bear before they could speak to a human counselor.

No Clues, Hard Bugs

''I haven't got a clue. What on earth is going on?'' If you really haven't any idea what could be wrong, life gets tougher.

Make the bug reproducible.

The first step is to make sure you can make the bug appear on demand. It's frustrating to chase down a bug that doesn't happen every time. Spend some time constructing input and parameter settings that reliably cause the problem, then wrap up the recipe so it can be run with a button push or a few keystrokes. If it's a hard bug, you'll be making it happen over and over as you track down the problem, so you'll save yourself time by making it easy to reproduce.

If the bug can't be made to happen every time, try to understand why not. Does some set of conditions make it happen more often than others? Even if you can't make it happen every time, if you can decrease the time spent waiting for it, you'll find it faster.

If a program provides debugging output, enable it. Simulation programs like the Markov chain program in Chapter 3 should include an option that produces debugging information such as the seed of the random number generator so that output can be reproduced; another option should allow for setting the seed. Many programs include such options and it is a good idea to include similar facilities in your own programs.

Divide and conquer.

Can the input that causes the program to fail be made smaller or more focused? Narrow down the possibilities by creating the smallest input where the bug still shows up. What changes make the error go away? Try to find crucial test cases that focus on the error. Each test case should aim at a definitive outcome that confirms or denies a specific hypothesis about what is wrong.

Proceed by binary search. Throw away half the input and see if the output is still wrong; if not, go back to the previous state and discard the other half of the input. The same binary search process can be used on the program text itself: eliminate some part of the program that should have no relationship to the bug and see if the bug is still there. An editor with undo is helpful in reducing big test cases and big programs without losing the bug.

Study the numerology of failures.

Sometimes a pattern in the numerology of failing examples gives a clue that focuses the search. We found some spelling mistakes in a newly written section of this book, where occasional letters had simply disappeared. This was mystifying. The text had been created by cutting and pasting from another file, so it seemed possible that something was wrong with the cut or paste commands in the text editor. But where to start looking for the problem? For clues we looked at the data, and noticed that the missing characters seemed uniformly distributed through the text. We measured the intervals and found that the distance between dropped characters was always 1023 bytes, a suspiciously non-random value. A search through the editor source code for numbers near 1024 found a couple of candidates. One of those was in new code, so we examined that first, and the bug was easy to spot, a classic off-by-one error where a null byte overwrote the last character in a 1024-byte buffer.

Studying the patterns of numbers related to the failure pointed us right at the bug. Elapsed time? A couple of minutes of mystification, five minutes of looking at the data to discover the pattern of missing characters, a minute to search for likely places to fix, and another minute to identify and eliminate the bug. This one would have been hopeless to find with a debugger, since it involved two multiprocess programs, driven by mouse clicks, communicating through a file system.

Display output to localize your search.

If you don't understand what the program is doing, adding statements to display more information can be the easiest, most cost-effective way to find out. Put them in to verify your understanding or refine your ideas of what's wrong. For example, display ''can't get here'' if you think it's not possible to reach a certain point in the code; then if you see that message, move the output statements back towards the start to figure out where things first begin to go wrong. Or show ''got here'' messages going forward, to find the last place where things seem to be working. Each message should be distinct so you can tell which one you're looking at.

Display messages in a compact fixed format so they are easy to scan by eye or with programs like the pattern-matching tool grep. (A grep-like program is invaluable for searching text. Chapter 9 includes a simple implementation.) If you're displaying the value of a variable, format it the same way each time. In C and C++, show pointers as hexadecimal numbers with %x or %p; this will help you to see whether two pointers have the same value or are related. Learn to read pointer values and recognize likely and unlikely ones, like zero, negative numbers, odd numbers, and small numbers. Familiarity with the form of addresses will pay off when you're using a debugger, too.

If output is potentially voluminous, it might be sufficient to print single-letter outputs like A, B, ..., as a compact display of where the program went.

Write self-checking code.

If more information is needed, you can write your own check function to test a condition, dump relevant variables, and abort the program:

/* check: test condition, print and die */
void check(char *s)
{
        if (var1 > var2) {
                printf("%s: var1 %d var2 %d\n", s, var1, var2);
                fflush(stdout); /* make sure all output is out */
                abort();        /* signal abnormal termination */
        }
}

We wrote check to call abort, a standard C library function that causes program execution to be terminated abnormally for analysis with a debugger. In a different application, you might want check to carry on after printing.

Next, add calls to check wherever they might be useful in your code:

check("before suspect");
/* ... suspect code ... */
check("after suspect");

After a bug is fixed, don't throw check away. Leave it in the source, commented out or controlled by a debugging option, so that it can be turned on again when the next difficult problem appears.

For harder problems, check might evolve to do verification and display of data structures. This approach can be generalized to routines that perform ongoing consistency checks of data structures and other information. In a program with intricate data structures, it's a good idea to write these checks before problems happen, as components of the program proper, so they can be turned on when trouble starts. Don't use them only when debugging; leave them installed during all stages of program development. If they're not expensive, it might be wise to leave them always enabled. Large programs like telephone switching systems often devote a significant amount of code to ''audit'' subsystems that monitor information and equipment, and report or even fix problems if they occur.

Write a log file.

Another tactic is to write a log file containing a fixed-format stream of debugging output. When a crash occurs, the log records what happened just before the crash. Web servers and other network programs maintain extensive logs of traffic so they can monitor themselves and their clients; this fragment (edited to fit) comes from a local system:

[Sun Dec 27 16:19:24 1998]
HTTPd: access to /usr/local/httpd/cgi-bin/test.html 
    failed for m1.cs.bell-labs.com, 
    reason: client denied by server (CGI non-executable)
    from http://m2.cs.bell-labs.com/cgi-bin/test.pl

Be sure to flush I/O buffers so the final log records appear in the log file. Output functions like printf normally buffer their output to print it efficiently; abnormal termination may discard this buffered output. In C, a call to fflush guarantees that all output is written before the program dies; there are analogous flush functions for output streams in C++ and Java. Or, if you can afford the overhead, you can avoid the flushing problem altogether by using unbuffered I/O for log files. The standard functions setbuf and setvbuf control buffering; setbuf(fp, NULL) turns off buffering on the stream fp. The standard error streams stderr, cerr, and System.err are normally unbuffered by default.

Draw a picture.

Sometimes pictures are more effective than text for testing and debugging. Pictures are especially helpful for understanding data structures, as we saw in Chapter 2, and of course when writing graphics software, but they can be used for all kinds of programs. Scatter plots display misplaced values more effectively than columns of numbers. A histogram of data reveals anomalies in exam grades, random numbers, bucket sizes in allocators and hash tables, and the like. If you don't understand what's happening inside your program, try annotating the data structures with statistics and plotting the result.

If you don't understand what's happening inside your program, try annotating the data structures with statistics and plotting the result. The following graphs plot, for the C Markov program in Chapter 3, hash chain lengths on the x axis and the number of elements in chains of that length on the y axis. The input data is our standard test, the Book of Psalms (42,685 words, 22,482 prefixes). The first two graphs are for the good hash multipliers of 31 and 37 and the third is for the awful multiplier of 128. In the first two cases, no chain is longer than 15 or 16 elements and most elements are in chains of length 5 or 6. In the third, the distribution is broader, the longest chain has 187 elements, and there are thousands of elements in chains longer than 20.

Use tools.

Make good use of the facilities of the environment where you are debugging. For example, a file comparison program like diff compares the outputs from successful and failed debugging runs so you can focus on what has changed. If your debugging output is long, use grep to search it or an editor to examine it. Resist the temptation to send debugging output to a printer: computers scan voluminous output better than people do. Use shell scripts and other tools to automate the processing of the output from debugging runs.

Write trivial programs to test hypotheses or confirm your understanding of how something works. For instance, is it valid to free a NULL pointer?

int main(void)
{
        free(NULL);
        return 0;
}

Source code control programs like RCS keep track of versions of code so you can see what has changed and revert to previous versions to restore a known state. Besides indicating what has changed recently, they can also identify sections of code that have a long history of frequent modification; these are often a good place for bugs to lurk.

Keep records.

If the search for a bug goes on for any length of time, you will begin to lose track of what you tried and what you learned. If you record your tests and results, you are less likely to overlook something or to think that you have checked some possibility when you haven't. The act of writing will help you remember the problem the next time something similar comes up, and will also serve when you're explaining it to someone else.

Non-reproducible Bugs

Bugs that won't stand still are the most difficult to deal with, and usually the problem isn't as obvious as failing hardware. ...

Occasionally hardware itself goes bad. The floating-point flaw in the 1994 Pentium processor that caused certain computations to produce wrong answers was a highly publicized and costly bug in the design of the hardware, but once it had been identified, it was of course reproducible. One of the strangest bugs we ever saw involved a calculator program, long ago on a two-processor system. Sometimes the expression 1/2 would print 0.5 and sometimes it would print some consistent but utterly wrong value like 0.7432; there was no pattern as to whether one got the right answer or the wrong one. The problem was eventually traced to a failure of the floating-point unit in one of the processors. As the calculator program was randomly executed on one processor or the other, answers were either correct or nonsense.

Many years ago we used a machine whose internal temperature could be estimated from the number of low-order bits it got wrong in floating-point calculations. One of the circuit cards was loose; as the machine got warmer, the card tilted further out of its socket, and more data bits were disconnected from the backplane.

Last Resorts

What do you do if none of this advice helps?

Buy the book!

Sample Pages

Download the sample pages (includes Chapter 3 and Index)

Table of Contents



1. Style.

Names.

Expressions and Statements.

Consistency and Idioms.

Function Macros.

Magic Numbers.

Comments.

Why Bother?



2. Algorithms and Data Structures.

Searching.

Sorting.

Libraries.

A Java Quicksort.

O-Notation.

Growing Arrays.

Lists.

Trees.

Hash Tables.

Summary.



3. Design and Implementation.

The Markov Chain Algorithm.

Data Structure Alternatives.

Building the Data Structure in C.

Generating Output.

Java.

C++.

Awk and Perl.

Performance.

Lessons.



4. Interfaces.

Comma-Separated Values.

A Prototype Library.

A Library for Others.

A C++ Implementation.

Interface Principles.

Resource Management.

Abort, Retry, Fail?

User Interfaces.



5. Debugging.

Debuggers.

Good Clues, Easy Bugs.

No Clues, Hard Bugs.

Last Resorts.

Non-reproducible Bugs.

Debugging Tools.

Other People's Bugs.

Summary.



6. Testing.

Test as You Write the Code.

Systematic Testing.

Test Automation.

Test Scaffolds.

Stress Tests.

Tips for Testing.

Who Does the Testing?

Testing the Markov Program.

Summary.



7. Performance.

A Bottleneck.

Timing and Profiling.

Strategies for Speed.

Tuning the Code.

Space Efficiency.

Estimation.

Summary.



8. Portability.

Language.

Headers and Libraries.

Program Organization.

Isolation.

Data Exchange.

Byte Order.

Portability and Upgrade.

Internationalization.

Summary.



9. Notation.

Formatting Data.

Regular Expressions.

Programmable Tools.

Interpreters, Compilers, and Virtual Machines.

Programs that Write Programs.

Using Macros to Generate Code.

Compiling on the Fly.



Epilogue.


Appendix: Collected Rules.


Index. 020161586XT04062001

Preface

Have you ever...

  • wasted a lot of time coding the wrong algorithm?
  • used a data structure that was much too complicated?
  • tested a program but missed an obvious problem?
  • spent a day looking for a bug you should have found in five minutes?
  • needed to make a program run three times faster and use less memory?
  • struggled to move a program from a workstation to a PC or vice versa?
  • tried to make a modest change in someone else's program?
  • rewritten a program because you couldn't understand it?

Was it fun?

These things happen to programmers all the time. But dealing with such problems is often harder than it should be because topics like testing, debugging, portability, performance, design alternatives, and style -- the practice of programming -- are not usually the focus of computer science or programming courses. Most programmers learn them haphazardly as their experience grows, and a few never learn them at all.

In a world of enormous and intricate interfaces, constantly changing tools and languages and systems, and relentless pressure for more of everything, one can lose sight of the basic principles -- simplicity, clarity, generality -- that form the bedrock of good software. One can also overlook the value of tools and notations that mechanize some of software creation and thus enlist the computer in its own programming.

Our approach in this book is based on these underlying, interrelated principles, which apply at all levels of computing. These include simplicity, which keeps programs short and manageable; clarity, which makes sure they are easy to understand, for people as well as machines; generality, which means they work well in a broad range of situations and adapt well as new situations arise; and automation, which lets the machine do the work for us, freeing us from mundane tasks. By looking at computer programming in a variety of languages, from algorithms and data structures through design, debugging, testing, and performance improvement, we can illustrate universal engineering concepts that are independent of language, operating system, or programming paradigm.

This book comes from many years of experience writing and maintaining a lot of software, teaching programming courses, and working with a wide variety of programmers. We want to share lessons about practical issues, to pass on insights from our experience, and to suggest ways for programmers of all levels to be more proficient and productive.

We are writing for several kinds of readers. If you are a student who has taken a programming course or two and would like to be a better programmer, this book will expand on some of the topics for which there wasn't enough time in school. If you write programs as part of your work, but in support of other activities rather than as the goal in itself, the information will help you to program more effectively. If you are a professional programmer who didn't get enough exposure to such topics in school or who would like a refresher, or if you are a software manager who wants to guide your staff in the right direction, the material here should be of value.

We hope that the advice will help you to write better programs. The only prerequisite is that you have done some programming, preferably in C, C++ or Java. Of course the more experience you have, the easier it will be; nothing can take you from neophyte to expert in 21 days. Unix and Linux programmers will find some of the examples more familiar than will those who have used only Windows and Macintosh systems, but programmers from any environment should discover things to make their lives easier.

The presentation is organized into nine chapters, each focusing on one major aspect of programming practice.

Chapter 1 discusses programming style. Good style is so important to good programming that we have chosen to cover it first. Well-written programs are better than badly-written ones -- they have fewer errors and are easier to debug and to modify -- so it is important to think about style from the beginning. This chapter also introduces an important theme in good programming, the use of idioms appropriate to the language being used.

Algorithms and data structures, the topics of Chapter 2, are the core of the computer science curriculum and a major part of programming courses. Since most readers will already be familiar with this material, our treatment is intended as a brief review of the handful of algorithms and data structures that show up in almost every program. More complex algorithms and data structures usually evolve from these building blocks, so one should master the basics.

Chapter 3 describes the design and implementation of a small program that illustrates algorithm and data structure issues in a realistic setting. The program is implemented in five languages; comparing the versions shows how the same data structures are handled in each, and how expressiveness and performance vary across a spectrum of languages.

Interfaces between users, programs, and parts of programs are fundamental in programming and much of the success of software is determined by how well interfaces are designed and implemented. Chapter 4 shows the evolution of a small library for parsing a widely used data format. Even though the example is small, it illustrates many of the concerns of interface design: abstraction, information hiding, resource management, and error handling.

Much as we try to write programs correctly the first time, bugs, and therefore debugging, are inevitable. Chapter 5 gives strategies and tactics for systematic and effective debugging. Among the topics are the signatures of common bugs and the importance of ''numerology,'' where patterns in debugging output often indicate where a problem lies.

Testing is an attempt to develop a reasonable assurance that a program is working correctly and that it stays correct as it evolves. The emphasis in Chapter 6 is on systematic testing by hand and machine. Boundary condition tests probe at potential weak spots. Mechanization and test scaffolds make it easy to do extensive testing with modest effort. Stress tests provide a different kind of testing than typical users do and ferret out a different class of bugs.

Computers are so fast and compilers are so good that many programs are fast enough the day they are written. But others are too slow, or they use too much memory, or both. Chapter 7 presents an orderly way to approach the task of making a program use resources efficiently, so that the program remains correct and sound as it is made more efficient.

Chapter 8 covers portability. Successful programs live long enough that their environment changes, or they must be moved to new systems or new hardware or new countries. The goal of portability is to reduce the maintenance of a program by minimizing the amount of change necessary to adapt it to a new environment.

Computing is rich in languages, not just the general-purpose ones that we use for the bulk of programming, but also many specialized languages that focus on narrow domains. Chapter 9 presents several examples of the importance of notation in computing, and shows how we can use it to simplify programs, to guide implementations, and even to help us write programs that write programs.
. . .

Copyright © 1999 Lucent Technologies. All rights reserved.

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