1. CSCI 3370: Principles of Programming Languages Introduction
Dr. Vamsi Paruchuri University of Central Arkansas
These slides are available at:

2. What is a Programming Language?
Definition: A programming language is a notational system for describing computation in machine-readable and human-readable form.

3. At the beginning, there was no programming languages…
Machine code example:
1st column: memory address
2nd column: machine code in
hex format

4. A question
What’s the best car?
What are the best shoes?

5. An answer Of course it depends on what you are doing
Programming languages have many goals, including making it easy in your domain to:
Write correct code
Write fast code
Write large projects
Interoperate …

6. Another question Aren’t all cars the same?
“4 wheels, a steering wheel, a brake – the rest is unimportant details”
- YES
- NO!!
Standards help (easy to build roads and rent a car)
But legacy issues dominate
(why are cars/roads the width they are?)

7. Aren’t all PLs the same? Almost every language is the same
Yes: Certain fundamentals appear almost everywhere (variables, abstraction, records, recursive definitions)
No: Real differences at formal and informal levels
TIOBE Programming Community Index:

8. Why? Increased capacity to express ideas
Improved background for choosing appropriate languages
Increased ability to learn new languages
Better understanding of the significance of implementation

9. Programming Domains Scientific Applications Business Applications
Fortran
Business Applications
COBOL
Artificial Intelligence
LISP, Prolog, Scheme
Systems Programming
ALGOL
Web Programming
JavaScript, PHP

10. Language Evaluation Criteria
Readability
The most important criterion
Factors:
Overall simplicity
Too many features is bad
Multiplicity of features is bad (e.g., a++, ++a)
Operator Overloading
Orthogonality
Makes the language easy to learn and read
Meaning is context independent
A relatively small set of primitive constructs can be combined in a relatively small number of ways
Every possible combination is legal
Lack of orthogonality leads to exceptions to rules

11. Language Evaluation Criteria
Readability factors (continued)
Control statements
goto Vs loops
Defining data types and structures
(e.g., Boolean variables)
Syntax considerations
Identifier forms and lengths
Special words
Form and meaning
grep !?

12. Language Evaluation Criteria
Writability
Factors:
Simplicity and orthogonality
Support for abstraction (Process and Data)
Expressivity (eg., count++ Vs. count = count+1)
Reliability
Type checking
Exception handling
Aliasing
Readability and writability

13. Language Evaluation Criteria
Cost
Categories
Training programmers to use language
Writing programs
Compiling programs
Executing programs
Optimization
Language implementation system
Reliability
Maintaining programs
Others: portability, generality, well- definedness

14. Picking a language
Admittedly, semantics can be far down the priority list:
What libraries are available?
What do management, clients want?
What is the de facto industry standard?
What does my team already know?
But:
Nice thing about class: we get to ignore all that
Technology leaders affect the answers
Sound reasoning about programs requires semantics
Mission-critical code doesn’t “seem to be right”
Blame: the compiler vendor or you?

15. Von Neumann Architecture
We use imperative languages, at least in part, because we use von Neumann machines
Data and programs stored in same memory
Memory is separate from CPU
Instructions and data are piped from memory to CPU
Basis for imperative languages
Variables model memory cells
Assignment statements model piping
Iteration is efficient
Because instructions are stored in adjacent cells

16. Von Neumann Architecture

17. Von Neumann Architecture
Stored-program computer in which an instruction fetch and a data operation cannot occur at the same time because they share a common bus.
This is referred to as the Von Neumann bottleneck and often limits the performance of the system.
On a large scale, the ability to treat instructions as data is what makes assemblers, compilers and other automated programming tools possible.
A stored-program design also allows for self- modifying code.

18. Von Neumann bottleneck
The shared bus between the program memory and data memory leads to the limited throughput (data transfer rate) between the CPU and memory compared to the amount of memory.
Because program memory and data memory cannot be accessed at same time, throughput is much smaller than rate at which the CPU can work.
This seriously limits the effective processing speed when the CPU is required to perform minimal processing on large amounts of data.
The CPU is continually forced to wait for needed data to be transferred to or from memory.

19. Influences on Language Design
Programming methodologies
1950s and early 1960s: Simple applications; worry about machine efficiency
Late 1960s: People efficiency became important; readability, better control structures
Structured programming
Top-down design and step-wise refinement
Late 1970s: Process-oriented to data-oriented
data abstraction
Middle 1980s: Object-oriented programming

20. Language Design Trade-Offs
Reliability vs. cost of execution
Array bounds
Readability vs. writability
Flexibility vs. safety
Pointers

21. Layered View of Computer

22. Language translation
Compiler: two-step process that translates source code into target code; then the user executes the target code.
Interpreter: one-step process in which the source code is executed directly.
Hybrids are also possible (Java, C#).

23. Compilation Process Translate high-level program to machine code - Slow translation - Fast execution

24. Implementation Methods
Pure interpretation
No translation
Slow execution
Becoming rare
E.g., JavaScript, PHP

25. Implementation Methods
Hybrid implementation systems
Small translation cost
Medium execution speed
E.g., Perl, Java JIT

26. Programming Environments
The collection of tools used in software development
UNIX
An older operating system and tool collection
Borland JBuilder
An integrated development environment for Java
Microsoft Visual Studio.NET
A large, complex visual environment
Used to program in C#, Visual BASIC.NET, Jscript, J#, or C++

27. Language definition Syntax: How to write the structure of a program.
Usually given a formal (i.e., mathematical) definition using a context-free language. (Lexical structure - the structure of the words or tokens - uses regular expressions.)
Semantics: What does it mean
the actual result of execution. Usually described in English, but can be done mathematically.
Semantics can have a static component: type checking, definition checking, other consistency checks prior to execution and a dynamic component.

28. Summary
Language description consists of Syntax Specification and a Semantics Specification.
Syntax describes what programs look like;
Semantics describes what they mean.