1. CSCE 314 Programming Languages
John Keyser
Spring 2016

2. About the course (syllabus overview)
Instructor
John Keyser
Office Hours
Generally, MW 2:00-3:00
Today: 1:30-2:30
Monday 1/25: cancelled TA
Mengyuan Chao
Peer Teachers
Other section (taught by Dylan Shell)

3. Course stuff Prerequisite: Textbooks Course webpage Piazza CSCE 221
Hutton: Programming in Haskell
Arnold, Gosling, Holmes: The Java Programming Language
Course webpage
Will be up soon (today?)
Piazza
Will be used for class discussions
Get a head start – sign up now...

4. Grades Exams (60%): Assignments (40%):
Midterm: 25% (just before Spring Break)
Final: 35% (at time assigned)
Final will be comprehensive, but weighted toward later material
Expect exams to be open book (details will come later)
Assignments (40%):
Turn in via CSNet
Late penalty: 10% first day, 20% second day, 100% thereafter

5. Collaboration Work should be your own The rest should be common sense…
Make sure you acknowledge any outside help

6. Understanding programming languages!
So, what’s the point of this course?
Understanding programming languages!

7. The Big Picture
Haskell
sum [] = 0
sum (x:xs) = x + sum xs
??????????????????????? C
a = 13
if (a>3)
c=10
ASSEMBLY
mov rdx, 7
mov eax, 3
jmp eax

8. Purpose of Programming Languages
To help PEOPLE communicate ideas
To the computer
To other people
To their future selves
Programming languages are designed to help people express ideas precisely and concisely.
The more “natural” the language is the better people can express ideas
Different languages do this in different ways

9. Abstraction
Haskell, Prolog sum[1..100] More Abstract Scheme, Java mynum.add(5) C i++; Assembly Language iadd Less Abstract Machine Language

10. History of programming languages
1940s: connecting wires to represent 0s and 1s
1950s: assemblers, FORTRAN, COBOL, LISP
1960s: ALGOL, BCPL(-> B -> C), SIMULA
1970s: Prolog, FP, ML, Miranda
1980s: Eiffel, C++
1990s: Haskell, Java, Python
2000s: D, C#, Spec#, F#, X10, Fortress, Scala, Ruby
2010s: Agda, Coq, …
The evolution has been and is toward higher levels of abstraction

11. Defining a Programming Language
Syntax: Defines the set of valid programs
Usually defined with the help of grammars and other conditions
if-statement ::= if cond-expr then stmt else stmt
| if cond-expr then stmt
cond-expr ::= . . .
stmt ::= . . .
Semantics: Defines the meaning of programs
Defined, e.g., as the effect of individual language constructs to the values of program variables
if cond then true-part else false-part
If cond evaluates to true, the meaning is that of true-part; if cond evaluates to false, the meaning is that of false-part.

12. Implementing a Programming Language
Task is to undo abstraction. From the source:
int i;
i = 2;
i = i + 7;
to assembly (this is actually Java bytecode):
iconst_2 // Put integer 2 on stack
istore_1 // Store the top stack value at location 1
iload_1 // Put the value at location 1 on stack
bipush 7 // Put the value 7 on the stack
iadd // Add two top stack values together
istore_1 // The sum, on top of stack, stored at location 1
to machine language:

13. Implementing a Programming Language – How to Undo the Abstraction
Source
program
Optimizer
I/O
Type
checker
Code
generator
Machine code
Lexer
Parser
Machine
Bytecode
JIT
Interpreter
Virtual machine
I/O
I/O

14. Compiling and Interpreting (1)
Typically compiled languages:
C, C++, Eiffel, FORTRAN
Java, C# (compiled to bytecode)
Typically interpreted languages:
Python, Perl, Prolog, LISP
Both compiled and interpreted:
Haskell, ML, Scheme

15. Compiling and Interpreting (2)
Borderline between interpretation and compilation not clear (not that important either)
Same goes with machine code vs. byte code.
Examples of modern compiling/interpreting/executing scenarios:
C and C++ can be compiled to LLVM bytecode
Java compiled to bytecode, bytecode interpreted by JVM, unless it is first JITted to native code, which can then be run on a virtual machine such as VMWare

16. Our Goal: To Understand Programming Languages!
How languages are designed - how they help people express ideas
Language constructs
Abstraction mechanisms
Efficiency considerations
How languages work – how they become the code that runs
Parsing
Internal program representation
Type checking
Interpretation
Formal verification – how we know that code will work

17. Learn ideas through examples
Functional language: Haskell
Object-oriented language: Java
These languages are the means, not the goals

18. Next time
More in-depth look at the “flowchart” of programs being processed
Begin looking at Haskell, a functional programming language