You are reading the textbook
Structure and Interpretation of Computer Programs
by Harold Abelson and Gerald Jay Sussman—with a twist.
The textbook emphasizes the importance of abstraction for managing
complexity, and introduces the reader to a host of concepts that
lie at the heart of the field of computer science.
Most of these ideas are independent of the language
in which programs are written, which employ the ideas for
solving computational problems. The twist then
consists of replacing the programming language that is used throughout
the text, to illustrate the ideas with actual computer programs.
While the authors used the programming language Scheme,
More precisely, this adaptation uses four tiny, carefully designed,
The languages are called Source §1,
Source §2, Source §3 and Source §4,
corresponding to the respective chapters 1, 2, 3 and 4 of the textbook.
The Source §1 language contains only constructs that are needed
in the programs contained in chapter 1: constructs required to
build abstractions with functions.
Source §2 is a superset of Source §1,
adding features required to build abstractions with data, on top
of the features of Source §1. Similarly,
Source §3 and 4 extend the previous language
features required to address the subject of the respective textbook chapter.
(Chapter 5 does not require any features beyond Source §4.)
language has many features that are not covered in this textbook. Indeed,
the Source languages are so small that they can be quite adequately described in
a few pages of text. The online folder
contains the specifications of the Source languages, as reference for
This textbook is interactive. Most programs are links. Clicking on them
takes the reader to a web-based programming environment called the
Academy. In the Source Academy, the reader can run the programs,
modify them and
experiment with them, without the need to install any software,
and without any requirements on the computer that they use, as
long as it comes with an internet browser.
course for first-year college or university students, even if one imposes
constraints on the language features to be covered. Students with prior
knowledge of the language are bound to make use of other features
in their programs. Their fellow students will legitimately ask the instructors
about those features, and any answer will either frustrate the
student or lead to a tangent that is most likely not conducive to the
which is not known for its systematic design. Our solution to this challenge
is radical: The Source Academy enforces the use of the respective
Source language when the student clicks on a program of a particular chapter.
Programs that use constructs beyond that language are rejected by the
Source Academy. This allows instructors of a SICP-based course
The original textbook was introduced to the National University of Singapore
by Jacob Katzenelson in 1997, as a more advanced alternative to the regular
Programming Methodology course offered to computer science
students. The course, known as
CS1101S since 1998,
methodology course for Computer Science undergraduate majors in 2018.
Preface to the Second Edition of SICP, 1996
Is it possible that software is not like anything else, that it
is meant to be discarded: that the whole point is to
always see it as a soap bubble?
Alan J. Perlis
The material in this book has been the basis of MIT's entry-level
computer science subject since 1980. We had been teaching this
material for four years when the first edition was published, and
twelve more years have elapsed until the appearance of this second
edition. We are pleased that our work has been widely adopted and
incorporated into other texts. We have seen our students take the
ideas and programs in this book and build them in as the core of new
computer systems and languages. In literal realization of an ancient
Talmudic pun, our students have become our builders. We are lucky to
have such capable students and such accomplished builders.
In preparing this edition, we have incorporated hundreds of
clarifications suggested by our own teaching experience and the
comments of colleagues at MIT and elsewhere. We have redesigned
most of the major programming systems in the book, including
the generic-arithmetic system, the interpreters, the register-machine
simulator, and the compiler; and we have rewritten all the program
examples to ensure that any Scheme implementation conforming to
the IEEE Scheme standard (IEEE 1990) will be able to run the code.
This edition emphasizes several new themes. The most important
of these is the central role played by different approaches to
dealing with time in computational models: objects with state,
concurrent programming, functional programming, lazy evaluation,
and nondeterministic programming. We have included new sections on
concurrency and nondeterminism, and we have tried to integrate this
theme throughout the book.
The first edition of the book closely followed the syllabus of our MIT
one-semester subject. With all the new material in the second
edition, it will not be possible to cover everything in a single
semester, so the instructor will have to pick and choose. In our own
teaching, we sometimes skip the section on logic programming
we have students use the
register-machine simulator but we do not cover its implementation
and we give only a cursory overview of
Even so, this is still
an intense course. Some instructors may wish to cover only the first
three or four chapters, leaving the other material for subsequent
The World-Wide-Web site of MIT Press
provides support for users of this book.
This includes programs from the book,
sample programming assignments, supplementary materials,
and downloadable implementations of the Scheme dialect of Lisp.
Harold Abelson and Gerald Jay Sussman
Preface to the First Edition of SICP, 1984
A computer is like a violin. You can imagine a novice trying first a
phonograph and then a violin. The latter, he says, sounds terrible.
That is the argument we have heard from our humanists and most of our
computer scientists. Computer programs are good, they say, for
particular purposes, but they aren't flexible. Neither is a violin,
or a typewriter, until you learn how to use it.
Why Programming Is a Good
Medium for Expressing Poorly-Understood and Sloppily-Formulated
The Structure and Interpretation of Computer Programs is the
entry-level subject in computer science at the Massachusetts Institute
of Technology. It is required of all students at MIT who major
in electrical engineering or in computer science, as one-fourth of the
common core curriculum, which also includes two subjects on
circuits and linear systems and a subject on the design of digital
systems. We have been involved in the development of this subject
since 1978, and we have taught this material in its present form since
the fall of 1980 to between 600 and 700 students each year. Most of
these students have had little or no prior formal training in
computation, although many have played with computers a bit and a few
have had extensive programming or hardware-design experience.
Our design of this introductory computer-science subject reflects two
major concerns. First, we want to establish the idea that a computer
language is not just a way of getting a computer to perform operations
but rather that it is a novel formal medium for expressing ideas about
methodology. Thus, programs must be written for people to read, and
only incidentally for machines to execute. Second, we believe that
the essential material to be addressed by a subject at this level is
not the syntax of particular programming-language constructs, nor
clever algorithms for computing particular functions efficiently, nor
even the mathematical analysis of algorithms and the foundations of
computing, but rather the techniques used to control the intellectual
complexity of large software systems.
Our goal is that students who complete this subject should have a good
feel for the elements of style and the aesthetics of programming.
They should have command of the major techniques for controlling
complexity in a large system. They should be capable of reading a
50-page-long program, if it is written in an exemplary style. They
should know what not to read, and what they need not understand at any
moment. They should feel secure about modifying a program, retaining
the spirit and style of the original author.
These skills are by no means unique to computer programming. The
techniques we teach and draw upon are common to all of engineering
design. We control complexity by building abstractions that hide
details when appropriate. We control complexity by establishing
conventional interfaces that enable us to construct systems by
combining standard, well-understood pieces in a
mix and match way.
We control complexity by establishing new languages for describing a
design, each of which emphasizes particular aspects of the design and
Underlying our approach to this subject is our conviction that
computer science is not a science and that its significance has
little to do with computers. The computer revolution is a revolution
in the way we think and in the way we express what we think. The
essence of this change is the emergence of what might best be called
procedural epistemology—the study of the structure of
knowledge from an imperative point of view, as opposed to the more
declarative point of view taken by classical mathematical subjects.
Mathematics provides a framework for dealing precisely with notions of
what is. Computation provides a framework for dealing precisely
with notions of
In teaching our material we use a dialect of the programming language
Lisp. We never formally teach the language, because we don't have to.
We just use it, and students pick it up in a few days. This is one
great advantage of Lisp-like languages: They have very few ways of
forming compound expressions, and almost no syntactic structure. All
of the formal properties can be covered in an hour, like the rules of
chess. After a short time we forget about syntactic details of the
language (because there are none) and get on with the real
issues—figuring out what we want to compute, how we will decompose
problems into manageable parts, and how we will work on the parts.
Another advantage of Lisp is that it supports (but does not enforce)
more of the large-scale strategies for modular decomposition of
programs than any other language we know. We can make procedural and
data abstractions, we can use higher-order functions to capture common
patterns of usage, we can model local state using assignment and data
mutation, we can link parts of a program with streams and delayed
evaluation, and we can easily implement embedded languages. All of
this is embedded in an interactive environment with excellent support
for incremental program design, construction, testing, and debugging.
We thank all the generations of Lisp wizards, starting with John
McCarthy, who have fashioned a fine tool of unprecedented power and
Scheme, the dialect of Lisp that we use, is an attempt to bring
together the power and elegance of Lisp and Algol. From Lisp we take
the metalinguistic power that derives from the simple syntax, the
uniform representation of programs as data objects, and the
garbage-collected heap-allocated data. From Algol we take lexical
scoping and block structure, which are gifts from the pioneers of
programming-language design who were on the Algol committee. We wish
to cite John Reynolds and Peter Landin for their insights into the
relationship of Church's lambda calculus to the structure of
programming languages. We also recognize our debt to the
mathematicians who scouted out this territory decades before computers
appeared on the scene. These pioneers include Alonzo Church, Barkley
Rosser, Stephen Kleene, and Haskell Curry.
Harold Abelson and Gerald Jay Sussman