Table of Contents
Chapter 1: Style
1.1 Names
1.2 Expressions and Statements
1.3 Consistency and Idioms
1.4 Function Macros
1.5 Magic Numbers
1.6 Comments
1.7 Why Bother?
Chapter 2: Algorithms and Data Structures
2.1 Searching
2.2 Sorting
2.3 Libraries
2.4 A Java Quicksort
2.5 O-Notation
2.6 Growing Arrays
2.7 Lists
2.8 Trees
2.9 Hash Tables
2.10 Summary
Chapter 3: Design and Implementation
3.1 The Markov Chain Algorithm
3.2 Data Structure Alternatives
3.3 Building the Data Structure in C
3.4 Generating Output
3.5 Java
3.6 C++
3.7 Awk and Perl
3.8 Performance
3.9 Lessons
Chapter 4: Interfaces
4.1 Comma-Separated Values
4.2 A Prototype Library
4.3 A Library for Others
4.4 A C++ Implementation
4.5 Interface Principles
4.6 Resource Management
4.7 Abort, Retry, Fail?
4.8 User Interfaces
Chapter 5: Debugging
5.1 Debuggers
5.2 Good Clues, Easy Bugs
5.3 No Clues, Hard Bugs
5.4 Last Resorts
5.5 Non-reproducible Bugs
5.6 Debugging Tools
5.7 Other People's Bugs
5.8 Summary
Chapter 6: Testing
6.1 Test as You Write the Code
6.2 Systematic Testing
6.3 Test Automation
6.4 Test Scaffolds
6.5 Stress Tests
6.6 Tips for Testing
6.7 Who Does the Testing?
6.8 Testing the Markov Program
6.9 Summary
Chapter 7: Performance
7.1 A Bottleneck
7.2 Timing and Profiling
7.3 Strategies for Speed
7.4 Tuning the Code
7.5 Space Efficiency
7.6 Estimation
7.7 Summary
Chapter 8: Portability
8.1 Language
8.2 Headers and Libraries
8.3 Program Organization
8.4 Isolation
8.5 Data Exchange
8.6 Byte Order
8.7 Portability and Upgrade
8.8 Internationalization
8.9 Summary
Chapter 9: Notation
9.1 Formatting Data
9.2 Regular Expressions
9.3 Programmable Tools
9.4 Interpreters, Compilers, and Virtual Machines
9.5 Programs that Write Programs
9.6 Using Macros to Generate Code
9.7 Compiling on the Fly
Epilogue
Appendix: Collected Rules
Index
Forewords & Introductions
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.
EXCERPT:
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.
Read an Excerpt
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.
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