Example PseudocodeProblem Given a sorted array a with n elements .docx

Example Pseudocode
Problem: Given a sorted array a with n elements (i.e., a[0] <=
a[1] <= … a[n-1]) and a number m, find if m is in the array.
1. Main pseudo code
data
given data
n: the number of integers given
a[0], …, a[n-1]: the given integers
m: given integer (to check if it is in a)
unknown data: N.A.
intermediate data:
found: indicating if m is found from a
plan
// get array a, n from user input (numbers in a must be
ordered).
n = getseries(a)
// find if m is in array a from index 0 to n-1
found = search(a, 0 , n-1, m)
if found print m is found in a.
Otherwise print m is not found in a.
(Pseudo code for all functions used in the main pseudocode)
2. Pseudo code for search function
Function name: search
input:
a: an array of numbers
bottom, top: bottom and top index
m: the number to search from a[bottom] to a[top]
output:
b: 1 if m is in a a[bottom] to a[top], 0 otherwise
Data
mid: middle index of the array
plan:

if (bottom > top) b = 0 and stop.
find the mid point mid of the array between bottom and top
if (a[mid] == m) b = 1
else if (m > a[mid])
P2.1 // find if m is in a from mid+1 to top:
b = search(a, mid+1, top, m)
else P2.2 // find if m is in a from bottom to mid-1
b = search(a, bottom, mid-1,m)
3. Pseudo code for getSeries function
omitted here
Programming Language Pragmatics
FOURTH EDITION
Michael L. Scott
Department of Computer Science
University of Rochester
Morgan Kaufmann is an imprint of Elsevier
225 Wyman Street,Waltham, MA 02451, USA
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ISBN: 978-0-12-410409-9
Copyright © 2016, 2009, 2006, 1999 Elsevier Inc.
About the Author
Michael L. Scott is a professor and past chair of the Department
of Computer Sci-
ence at the University of Rochester. He received his Ph.D. in
computer sciences in
1985 from the University of Wisconsin–Madison. From 2014–
2015 he was a Vis-
iting Scientist at Google. His research interests lie at the
intersection of program-
ming languages, operating systems, and high-level computer
architecture, with an
emphasis on parallel and distributed computing. His MCS
mutual exclusion lock,
co-designed with John Mellor-Crummey, is used in a variety of
commercial and
academic systems. Several other algorithms, co-designed with
Maged Michael,
Bill Scherer, and Doug Lea, appear in the java.util.concurrent
standard li-
brary. In 2006 he and Dr. Mellor-Crummey shared the ACM
SIGACT/SIGOPS
Edsger W. Dijkstra Prize in Distributed Computing.
Dr. Scott is a Fellow of the Association for Computing
Machinery, a Fellow of
the Institute of Electrical and Electronics Engineers, and a

member of Usenix, the
Union of Concerned Scientists, and the American Association of
University Pro-
fessors. The author of more than 150 refereed publications, he
served as General
Chair of the 2003 ACM Symposium on Operating Systems
Principles (SOSP) and
as Program Chair of the 2007 ACM SIGPLAN Workshop on
Transactional Com-
puting (TRANSACT), the 2008 ACM SIGPLAN Symposium on
Principles and
Practice of Parallel Programming (PPoPP), and the 2012
International Confer-
ence on Architectural Support for Programming Languages and
Operating Sys-
tems (ASPLOS). In 2001 he received the University of
Rochester’s Robert and
Pamela Goergen Award for Distinguished Achievement and
Artistry in Under-
graduate Teaching.
Contents
Foreword xxiii
Preface xxv
I FOUNDATIONS 3
1 Introduction 5
1.1 The Art of Language Design 7
1.2 The Programming Language Spectrum 11

1.3 Why Study Programming Languages? 14
1.4 Compilation and Interpretation 17
1.5 Programming Environments 24
1.6 An Overview of Compilation 26
1.6.1 Lexical and Syntax Analysis 28
1.6.2 Semantic Analysis and Intermediate Code Generation 32
1.6.3 Target Code Generation 34
1.6.4 Code Improvement 36
1.7 Summary and Concluding Remarks 37
1.8 Exercises 38
1.9 Explorations 39
1.10 Bibliographic Notes 40
2 Programming Language Syntax 43
2.1 Specifying Syntax: Regular Expressions and Context-Free
Grammars 44
2.1.1 Tokens and Regular Expressions 45
2.1.2 Context-Free Grammars 48
2.1.3 Derivations and Parse Trees 50
2.2 Scanning 54
2.2.1 Generating a Finite Automaton 56
2.2.2 Scanner Code 61
2.2.3 Table-Driven Scanning 65
2.2.4 Lexical Errors 65
2.2.5 Pragmas 67

2.3 Parsing 69
2.3.1 Recursive Descent 73
2.3.2 Writing an LL(1) Grammar 79
2.3.3 Table-Driven Top-Down Parsing 82
2.3.4 Bottom-Up Parsing 89
2.3.5 Syntax Errors C 1 . 102
2.4 Theoretical Foundations C 13 . 103
2.4.1 Finite Automata C 13
2.4.2 Push-Down Automata C 18
2.4.3 Grammar and Language Classes C 19
2.6 Exercises 105
2.7 Explorations 112
3 Names, Scopes, and Bindings 115
3.1 The Notion of Binding Time 116
3.2 Object Lifetime and Storage Management 118
3.2.1 Static Allocation 119
3.2.2 Stack-Based Allocation 120
3.2.3 Heap-Based Allocation 122
3.2.4 Garbage Collection 124
3.3 Scope Rules 125
3.3.1 Static Scoping 126
3.3.2 Nested Subroutines 127
3.3.3 Declaration Order 130
3.3.4 Modules 135

3.3.5 Module Types and Classes 139
3.3.6 Dynamic Scoping 142
3.4 Implementing Scope C 26 . 144
3.4.1 Symbol Tables C 26
3.4.2 Association Lists and Central Reference Tables C 31
3.5 The Meaning of Names within a Scope 145
3.5.1 Aliases 145
3.5.2 Overloading 147
3.6 The Binding of Referencing Environments 152
3.6.1 Subroutine Closures 153
3.6.2 First-Class Values and Unlimited Extent 155
3.6.3 Object Closures 157
3.6.4 Lambda Expressions 159
3.7 Macro Expansion 162
3.8 Separate Compilation C 36 . 165
3.8.1 Separate Compilation in C C 37
3.8.2 Packages and Automatic Header Inference C 40
3.8.3 Module Hierarchies C 41
3.10 Exercises 167
4 Semantic Analysis 179

4.1 The Role of the Semantic Analyzer 180
4.2 Attribute Grammars 184
4.3 Evaluating Attributes 187
4.4 Action Routines 195
4.5 Space Management for Attributes C 45 . 200
4.5.1 Bottom-Up Evaluation C 45
4.5.2 Top-Down Evaluation C 50
4.6 Tree Grammars and Syntax Tree Decoration 201
4.8 Exercises 209
5 Target Machine Architecture C 60 . 217
5.1 The Memory Hierarchy C 61
5.2 Data Representation C 63
5.2.1 Integer Arithmetic C 65
5.2.2 Floating-Point Arithmetic C 67
5.3 Instruction Set Architecture (ISA) C 70
5.3.1 Addressing Modes C 71
5.3.2 Conditions and Branches C 72

5.4 Architecture and Implementation C 75
5.4.1 Microprogramming C 76
5.4.2 Microprocessors C 77
5.4.3 RISC C 77
5.4.4 Multithreading and Multicore C 78
5.4.5 Two Example Architectures: The x86 and ARM C 80
5.5 Compiling for Modern Processors C 88
5.5.1 Keeping the Pipeline Full C 89
5.5.2 Register Allocation C 93
5.6 Summary and Concluding Remarks C 98
5.7 Exercises C 100
5.8 Explorations C 104
5.9 Bibliographic Notes C 105
II CORE ISSUES IN LANGUAGE DESIGN 221
6 Control Flow 223
6.1 Expression Evaluation 224
6.1.1 Precedence and Associativity 226
6.1.2 Assignments 229
6.1.3 Initialization 238
6.1.4 Ordering within Expressions 240
6.1.5 Short-Circuit Evaluation 243
6.2 Structured and Unstructured Flow 246
6.2.1 Structured Alternatives to goto 247
6.2.2 Continuations 250
6.3 Sequencing 252
6.4 Selection 253

6.4.1 Short-Circuited Conditions 254
6.4.2 Case/Switch Statements 256
6.5 Iteration 261
6.5.1 Enumeration-Controlled Loops 262
6.5.2 Combination Loops 266
6.5.3 Iterators 268
6.5.4 Generators in Icon C 107 . 274
6.5.5 Logically Controlled Loops 275
6.6 Recursion 277
6.6.1 Iteration and Recursion 277
6.6.2 Applicative- and Normal-Order Evaluation 282
6.7 Nondeterminacy C 110 . 283
6.9 Exercises 286
7 Type Systems 297
7.1 Overview 298
7.1.1 The Meaning of “Type” 300
7.1.2 Polymorphism 302
7.1.3 Orthogonality 302
7.1.4 Classification of Types 305
7.2 Type Checking 312
7.2.1 Type Equivalence 313

7.2.2 Type Compatibility 320
7.2.3 Type Inference 324
7.2.4 Type Checking in ML 326
7.3 Parametric Polymorphism 331
7.3.1 Generic Subroutines and Classes 333
7.3.2 Generics in C++, Java, and C# C 119 . 339
7.4 Equality Testing and Assignment 340
7.6 Exercises 344
8 Composite Types 351
8.1 Records (Structures) 351
8.1.1 Syntax and Operations 352
8.1.2 Memory Layout and Its Impact 353
8.1.3 Variant Records (Unions) C 136 . 357
8.2 Arrays 359
8.2.2 Dimensions, Bounds, and Allocation 363
8.2.3 Memory Layout 368
8.3 Strings 375
8.4 Sets 376

8.5 Pointers and Recursive Types 377
8.5.2 Dangling References C 144 . 388
8.6 Lists 398
8.7 Files and Input/Output C 148 . 401
8.7.1 Interactive I/O C 148
8.7.2 File-Based I/O C 149
8.7.3 Text I/O C 151
8.9 Exercises 404
9 Subroutines and Control Abstraction 411
9.1 Review of Stack Layout 412
9.2 Calling Sequences 414
9.2.1 Displays C 163 . 417
9.2.2 Stack Case Studies: LLVM on ARM; gcc on x86 C 167 .
417
9.2.3 Register Windows C 177 . 419
9.2.4 In-Line Expansion 419
9.3 Parameter Passing 422
9.3.1 Parameter Modes 423
9.3.2 Call by Name C 180 . 433
9.3.3 Special-Purpose Parameters 433

9.3.4 Function Returns 438
9.4 Exception Handling 440
9.4.1 Defining Exceptions 444
9.4.2 Exception Propagation 445
9.4.3 Implementation of Exceptions 447
9.5 Coroutines 450
9.5.1 Stack Allocation 453
9.5.2 Transfer 454
9.5.3 Implementation of Iterators C 183 . 456
9.5.4 Discrete Event Simulation C 187 . 456
9.6 Events 456
9.6.1 Sequential Handlers 457
9.6.2 Thread-Based Handlers 459
9.8 Exercises 462
10 Data Abstraction and Object Orientation 471
10.1 Object-Oriented Programming 473
10.1.1 Classes and Generics 481
10.2 Encapsulation and Inheritance 485
10.2.1 Modules 486
10.2.2 Classes 488

10.2.3 Nesting (Inner Classes) 490
10.2.4 Type Extensions 491
10.2.5 Extending without Inheritance 494
10.3 Initialization and Finalization 495
10.3.1 Choosing a Constructor 496
10.3.2 References and Values 498
10.3.3 Execution Order 502
10.4 Dynamic Method Binding 505
10.4.1 Virtual and Nonvirtual Methods 508
10.4.2 Abstract Classes 508
10.4.3 Member Lookup 509
10.4.4 Object Closures 513
10.5 Mix-In Inheritance 516
10.5.1 Implementation 517
10.5.2 Extensions 519
10.6 True Multiple Inheritance C 194 . 521
10.6.1 Semantic Ambiguities C 196
10.6.2 Replicated Inheritance C 200
10.6.3 Shared Inheritance C 201
10.7 Object-Oriented Programming Revisited 522
10.7.1 The Object Model of Smalltalk C 204 . 523
10.9 Exercises 525

III ALTERNATIVE PROGRAMMING MODELS 533
11 Functional Languages 535
11.1 Historical Origins 536
11.2 Functional Programming Concepts 537
11.3 A Bit of Scheme 539
11.3.1 Bindings 542
11.3.2 Lists and Numbers 543
11.3.3 Equality Testing and Searching 544
11.3.4 Control Flow and Assignment 545
11.3.5 Programs as Lists 547
11.3.6 Extended Example: DFA Simulation in Scheme 548
11.4 A Bit of OCaml 550
11.4.1 Equality and Ordering 553
11.4.2 Bindings and Lambda Expressions 554
11.4.3 Type Constructors 555
11.4.4 Pattern Matching 559
11.4.5 Control Flow and Side Effects 563
11.4.6 Extended Example: DFA Simulation in OCaml 565
11.5 Evaluation Order Revisited 567
11.5.1 Strictness and Lazy Evaluation 569
11.5.2 I/O: Streams and Monads 571
11.6 Higher-Order Functions 576
11.7.1 Lambda Calculus C 214

11.7.2 Control Flow C 217
11.7.3 Structures C 219
11.8 Functional Programming in Perspective 581
11.10 Exercises 584
12 Logic Languages 591
12.1 Logic Programming Concepts 592
12.2 Prolog 593
12.2.1 Resolution and Unification 595
12.2.2 Lists 596
12.2.3 Arithmetic 597
12.2.4 Search/Execution Order 598
12.2.5 Extended Example: Tic-Tac-Toe 600
12.2.6 Imperative Control Flow 604
12.2.7 Database Manipulation 607
12.3.1 Clausal Form C 227
12.3.2 Limitations C 228
12.3.3 Skolemization C 230
12.4 Logic Programming in Perspective 613
12.4.1 Parts of Logic Not Covered 613
12.4.2 Execution Order 613
12.4.3 Negation and the “Closed World” Assumption 615

12.6 Exercises 618
13 Concurrency 623
13.1 Background and Motivation 624
13.1.1 The Case for Multithreaded Programs 627
13.1.2 Multiprocessor Architecture 631
13.2 Concurrent Programming Fundamentals 635
13.2.1 Communication and Synchronization 635
13.2.2 Languages and Libraries 637
13.2.3 Thread Creation Syntax 638
13.2.4 Implementation of Threads 647
13.3 Implementing Synchronization 652
13.3.1 Busy-Wait Synchronization 653
13.3.2 Nonblocking Algorithms 657
13.3.3 Memory Consistency 659
13.3.4 Scheduler Implementation 663
13.3.5 Semaphores 667
13.4 Language-Level Constructs 669
13.4.1 Monitors 669
13.4.2 Conditional Critical Regions 674
13.4.3 Synchronization in Java 676
13.4.4 Transactional Memory 679
13.4.5 Implicit Synchronization 683

13.5 Message Passing C 235 . 687
13.5.1 Naming Communication Partners C 235
13.5.2 Sending C 239
13.5.3 Receiving C 244
13.5.4 Remote Procedure Call C 249
13.7 Exercises 690
14 Scripting Languages 699
14.1 What Is a Scripting Language? 700
14.1.1 Common Characteristics 701
14.2 Problem Domains 704
14.2.1 Shell (Command) Languages 705
14.2.2 Text Processing and Report Generation 712
14.2.3 Mathematics and Statistics 717
14.2.4 “Glue” Languages and General-Purpose Scripting 718
14.2.5 Extension Languages 724
14.3 Scripting the World Wide Web 727
14.3.1 CGI Scripts 728
14.3.2 Embedded Server-Side Scripts 729
14.3.3 Client-Side Scripts 734
14.3.4 Java Applets and Other Embedded Elements 734
14.3.5 XSLT C 258 . 736

14.4 Innovative Features 738
14.4.1 Names and Scopes 739
14.4.2 String and Pattern Manipulation 743
14.4.3 Data Types 751
14.4.4 Object Orientation 757
14.6 Exercises 765
IV A CLOSER LOOK AT IMPLEMENTATION 773
15 Building a Runnable Program 775
15.1 Back-End Compiler Structure 775
15.1.1 A Plausible Set of Phases 776
15.1.2 Phases and Passes 780
15.2 Intermediate Forms 780
15.2.1 GIMPLE and RTL C 273 . 782
15.2.2 Stack-Based Intermediate Forms 782
15.3 Code Generation 784
15.3.1 An Attribute Grammar Example 785
15.3.2 Register Allocation 787
15.4 Address Space Organization 790
15.5 Assembly 792
15.5.1 Emitting Instructions 794
15.5.2 Assigning Addresses to Names 796

15.6 Linking 797
15.6.1 Relocation and Name Resolution 798
15.6.2 Type Checking 799
15.7 Dynamic Linking C 279 . 800
15.7.1 Position-Independent Code C 280
15.7.2 Fully Dynamic (Lazy) Linking C 282
15.9 Exercises 803
16 Run-Time Program Management 807
16.1 Virtual Machines 810
16.1.1 The Java Virtual Machine 812
16.1.2 The Common Language Infrastructure C 286 . 820
16.2 Late Binding of Machine Code 822
16.2.1 Just-in-Time and Dynamic Compilation 822
16.2.2 Binary Translation 828
16.2.3 Binary Rewriting 833
16.2.4 Mobile Code and Sandboxing 835
16.3 Inspection/Introspection 837
16.3.1 Reflection 837
16.3.2 Symbolic Debugging 845
16.3.3 Performance Analysis 848

16.5 Exercises 851
17 Code Improvement C 297 . 857
17.1 Phases of Code Improvement C 299
17.2 Peephole Optimization C 301
17.3 Redundancy Elimination in Basic Blocks C 304
17.3.1 A Running Example C 305
17.3.2 Value Numbering C 307
17.4 Global Redundancy and Data Flow Analysis C 312
17.4.1 SSA Form and Global Value Numbering C 312
17.4.2 Global Common Subexpression Elimination C 315
17.5 Loop Improvement I C 323
17.5.1 Loop Invariants C 323
17.5.2 Induction Variables C 325
17.6 Instruction Scheduling C 328
17.7 Loop Improvement II C 332
17.7.1 Loop Unrolling and Software Pipelining C 332
17.7.2 Loop Reordering C 337
17.8 Register Allocation C 344
17.9 Summary and Concluding Remarks C 348

17.10 Exercises C 349
17.11 Explorations C 353
17.12 Bibliographic Notes C 354
A Programming Languages Mentioned 859
B Language Design and Language Implementation 871
C Numbered Examples 877
Bibliography 891
Index 911
Foreword
Programming languages are universally accepted as one of the
core subjects that
every computer scientist must master. The reason is clear: these
languages are
the main notation we use for developing products and for
communicating new
ideas. They have influenced the field by enabling the
development of those
multimillion-line programs that shaped the information age.
Their success is
owed to the long-standing effort of the computer science
community in the cre-
ation of new languages and in the development of strategies for
their implemen-
tation. The large number of computer scientists mentioned in
the footnotes and

bibliographic notes in this book by Michael Scott is a clear
manifestation of the
magnitude of this effort as is the sheer number and diversity of
topics it contains.
Over 75 programming languages are discussed. They represent
the best and
most influential contributions in language design across time,
paradigms, and ap-
plication domains. They are the outcome of decades of work
that led initially to
Fortran and Lisp in the 1950s, to numerous languages in the
years that followed,
and, in our times, to the popular dynamic languages used to
program the Web.
The 75 plus languages span numerous paradigms including
imperative, func-
tional, logic, static, dynamic, sequential, shared-memory
parallel, distributed-
memory parallel, dataflow, high-level, and intermediate
languages. They include
languages for scientific computing, for symbolic manipulations,
and for accessing
databases. This rich diversity of languages is crucial for
programmer productivity
and is one of the great assets of the discipline of computing.
Cutting across languages, this book presents a detailed
discussion of control
flow, types, and abstraction mechanisms. These are the
representations needed
to develop programs that are well organized, modular, easy to
understand, and
easy to maintain. Knowledge of these core features and of their
incarnation in to-
day’s languages is a basic foundation to be an effective

programmer and to better
understand computer science today.
Strategies to implement programming languages must be studied
together
with the design paradigms. A reason is that success of a
language depends on
the quality of its implementation. Also, the capabilities of these
strategies some-
times constraint the design of languages. The implementation of
a language starts
with parsing and lexical scanning needed to compute the
syntactic structure of
programs. Today’s parsing techniques, described in Part I, are
among the most
beautiful algorithms ever developed and are a great example of
the use of mathe-
matical objects to create practical instruments. They are
worthwhile studying just
as an intellectual achievement. They are of course of great
practical value, and a
good way to appreciate the greatness of these strategies is to go
back to the first
Fortran compiler and study the ad hoc, albeit highly ingenious,
strategy used to
implement precedence of operators by the pioneers that built
that compiler.
The other usual component of implementation are the compiler
components
that carry out the translation from the high-level language
representation to a
lower level form suitable for execution by real or virtual

machines. The transla-
tion can be done ahead of time, during execution (just in time),
or both. The
book discusses these approaches and implementation strategies
including the
elegant mechanisms of translation driven by parsing. To
produce highly effi-
cient code, translation routines apply strategies to avoid
redundant computations,
make efficient use of the memory hierarchy, and take advantage
of intra-processor
parallelism. These, sometimes conflicting goals, are undertaken
by the optimiza-
tion components of compilers. Although this topic is typically
outside the scope
of a first course on compilers, the book gives the reader access
to a good overview
of program optimization in Part IV.
An important recent development in computing is the
popularization of paral-
lelism and the expectation that, in the foreseeable future,
performance gains will
mainly be the result of effectively exploiting this parallelism.
The book responds
to this development by presenting the reader with a range of
topics in concurrent
programming including mechanisms for synchronization,
communication, and
coordination across threads. This information will become
increasingly impor-
tant as parallelism consolidates as the norm in computing.
Programming languages are the bridge between programmers
and machines.
It is in them that algorithms must be represented for execution.

The study of pro-
gramming languages design and implementation offers great
educational value
by requiring an understanding of the strategies used to connect
the different as-
pects of computing. By presenting such an extensive treatment
of the subject,
Michael Scott’s Programming Language Pragmatics, is a great
contribution to the
literature and a valuable source of information for computer
scientists.
David Padua
Siebel Center for Computer Science
University of Illinois at Urbana-Champaign
Preface
A course in computer programming provides the typical
student’s first ex-
posure to the field of computer science. Most students in such a
course will have
used computers all their lives, for social networking, email,
games, web brows-
ing, word processing, and a host of other tasks, but it is not
until they write their
first programs that they begin to appreciate how applications
work. After gaining
a certain level of facility as programmers (presumably with the
help of a good
course in data structures and algorithms), the natural next step
is to wonder how
programming languages work. This book provides an

explanation. It aims, quite
simply, to be the most comprehensive and accurate languages
text available, in a
style that is engaging and accessible to the typical
undergraduate. This aim re-
flects my conviction that students will understand more, and
enjoy the material
more, if we explain what is really going on.
In the conventional “systems” curriculum, the material beyond
data struc-
tures (and possibly computer organization) tends to be
compartmentalized into a
host of separate subjects, including programming languages,
compiler construc-
tion, computer architecture, operating systems, networks,
parallel and distributed
computing, database management systems, and possibly
software engineering,
object-oriented design, graphics, or user interface systems. One
problem with
this compartmentalization is that the list of subjects keeps
growing, but the num-
ber of semesters in a Bachelor’s program does not. More
important, perhaps,
many of the most interesting discoveries in computer science
occur at the bound-
aries between subjects. Computer architecture and compiler
construction, for
example, have inspired each other for over 50 years, through
generations of su-
percomputers, pipelined microprocessors, multicore chips, and
modern GPUs.
Over the past decade, advances in virtualization have blurred
boundaries among
the hardware, operating system, compiler, and language run-

time system, and
have spurred the explosion in cloud computing. Programming
language tech-
nology is now routinely embedded in everything from dynamic
web content, to
gaming and entertainment, to security and finance.
Increasingly, both educators and practitioners have come to
emphasize these
sorts of interactions. Within higher education in particular,
there is a growing
trend toward integration in the core curriculum. Rather than
give the typical stu-
dent an in-depth look at two or three narrow subjects, leaving
holes in all the
others, many schools have revised the programming languages
and computer or-
ganization courses to cover a wider range of topics, with
follow-on electives in
various specializations. This trend is very much in keeping with
the ACM/IEEE-
CS Computer Science Curricula 2013 guidelines [SR13], which
emphasize the need
to manage the size of the curriculum and to cultivate both a
“system-level per-
spective” and an appreciation of the interplay between theory
and practice. In
particular, the authors write,
Graduates of a computer science program need to think at
multiple levels of detail and
abstraction. This understanding should transcend the
implementation details of the

various components to encompass an appreciation for the
structure of computer systems
and the processes involved in their construction and analysis [p.
24].
On the specific subject of this text, they write
Programming languages are the medium through which
programmers precisely describe
concepts, formulate algorithms, and reason about solutions. In
the course of a career,
a computer scientist will work with many different languages,
separately or together.
Software developers must understand the programming models
underlying different
languages and make informed design choices in languages
supporting multiple com-
plementary approaches. Computer scientists will often need to
learn new languages
and programming constructs, and must understand the principles
underlying how pro-
gramming language features are defined, composed, and
implemented. The effective
use of programming languages, and appreciation of their
limitations, also requires a ba-
sic knowledge of programming language translation and static
program analysis, as well
as run-time components such as memory management [p. 155].
The first three editions of Programming Language Pragmatics
(PLP) had the
good fortune of riding the trend toward integrated
understanding. This fourth
edition continues and strengthens the “systems perspective”
while preserving the
central focus on programming language design.

At its core, PLP is a book about how programming languages
work. Rather
than enumerate the details of many different languages, it
focuses on concepts
that underlie all the languages the student is likely to encounter,
illustrating those
concepts with a variety of concrete examples, and exploring the
tradeoffs that ex-
plain why different languages were designed in different ways.
Similarly, rather
than explain how to build a compiler or interpreter (a task few
programmers will
undertake in its entirety), PLP focuses on what a compiler does
to an input pro-
gram, and why. Language design and implementation are thus
explored together,
with an emphasis on the ways in which they interact.
Changes in the Fourth Edition
In comparison to the third edition, PLP-4e includes
1. New chapters devoted to type systems and composite types,
in place of the
older single chapter on types
2. Updated treatment of functional programming, with extensive
coverage of
OCaml
3. Numerous other reflections of changes in the field
4. Improvements inspired by instructor feedback or a fresh
consideration of fa-

miliar topics
Item 1 in this list is perhaps the most visible change. Chapter 7
was the longest
in previous editions, and there is a natural split in the subject
material. Reorgani-
zation of this material for PLP-4e afforded an opportunity to
devote more explicit
attention to the subject of type inference, and of its role in ML-
family languages
in particular. It also facilitated an update and reorganization of
the material on
parametric polymorphism, which was previously scattered
across several differ-
ent chapters and sections.
Item 2 reflects the increasing adoption of functional techniques
into main-
stream imperative languages, as well as the increasing
prominence of SML,
OCaml, and Haskell in both education and industry. Throughout
the text,
OCaml is now co-equal with Scheme as a source of functional
programming
examples. As noted in the previous paragraph, there is an
expanded section
(7.2.4) on the ML type system, and Section 11.4 includes an
OCaml overview,
with coverage of equality and ordering, bindings and lambda
expressions, type
constructors, pattern matching, and control flow and side
effects. The choice of
OCaml, rather than Haskell, as the ML-family exemplar reflects
its prominence in
industry, together with classroom experience suggesting that—

at least for many
students—the initial exposure to functional thinking is easier in
the context of
eager evaluation. To colleagues who wish I’d chosen Haskell,
my apologies!
Other new material (Item 3) appears throughout the text.
Wherever appro-
priate, reference has been made to features of the latest
languages and standards,
including C & C++11, Java 8, C# 5, Scala, Go, Swift, Python 3,
and HTML 5.
Section 3.6.4 pulls together previously scattered coverage of
lambda expressions,
and shows how these have been added to various imperative
languages. Com-
plementary coverage of object closures, including C++11’s
std::function and
std::bind, appears in Section 10.4.4. Section c-5.4.5 introduces
the x86-64 and
ARM architectures in place of the x86-32 and MIPS used in
previous editions. Ex-
amples using these same two architectures subsequently appear
in the sections on
calling sequences (9.2) and linking (15.6). Coverage of the x86
calling sequence
continues to rely on gcc; the ARM case study uses LLVM.
Section 8.5.3 intro-
duces smart pointers. R-value references appear in Section
9.3.1. JavaFX replaces
Swing in the graphics examples of Section 9.6.2. Appendix A
has new entries for
Go, Lua, Rust, Scala, and Swift.
Finally, Item 4 encompasses improvements to almost every
section of the

text. Among the more heavily updated topics are FOLLOW and
PREDICT sets
(Section 2.3.3); Wirth’s error recovery algorithm for recursive
descent (Sec-
tion c-2.3.5); overloading (Section 3.5.2); modules (Section
3.3.4); duck typing
(Section 7.3); records and variants (Section 8.1); intrusive lists
(removed from
the running example of Chapter 10); static fields and methods
(Section 10.2.2);
mix-in …

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