Data Types Data Types Every program uses data, either explicitly or - PowerPoint PPT Presentation

Data Types

Data Types  Every program uses data, either explicitly or implicitly to arrive at a result.  Data in a program is collected into data structures, and is manipulated by algorithms. Algorithms + Data Structures = Programs 2

Data Types  Most programming languages provide:  A set of simple data entities such as  Integer  Real  Boolean  Char  Mechanisms for constructing new data entities from these. 3

Data Types • Finiteness: – Mathematically, integer data is thought of as: • …, -3, -2, -1, 0, 1, 2, 3, … • In a computer ’ s hardware, there is always an upper and lower bound for values of integers. • This is due to a given representation size in bits of an integer on a specific machine. 4

Data Types  Different language designs deal with data type information in different ways.  Some languages mandate that type information be explicit in the programming language. This is used to verify program correctness prior to execution. Example:  int x;  float y;  char z; 5

Data Types  Scheme is a language with no explicit types or translation-time typing.  Ada is a very strictly-typed language. It performs strict type checking. 6

Data Types  Some form of static (translation-time) type checking is very useful to:  Allocate memory efficiently.  Reduce the amount of code to be compiled.  Improves both writability and readability.  Improves security and reliability.  Removes ambiguities.  Can be used to prove program correctness. 7

Data Types A Data Type is a Set of Values Insufficient Definition! 8

Data Types A Data Type is a Set of Values + A Set of Operations on Those Values having Certain Properties 9

Data Types  Example:  The values of an int range from a lower bound to an upper bound (e.g. MININT to MAXINT)  Operations on int include:  +  -  *  /  Etc. 10

Data Types  Type Checking:  Is the process a translator goes through to determine whether the type information in a program is consistent. 11

Data Types  Type Checking:  Example:  x = y + z  Can the type of the result of y + z be stored in x?  The type of y + z depends on the type of y and z  Determining the type of y + z is called type inference. 12

Data Types  We can construct more complex user defined types from simple data types:  Example:  An array of ten integers in C/C++:  int a[10]  The same array in Ada:  a: array (1..10) of integer 13

Data Types  Type definitions:  In some languages, we can give new types names.  Example in C: A name given to an int array of ten elements typedef int Array_of_ten_integers[10]; Array_of_ten_integers a; Just like we declare int a, We can declare Array_of_ten_integers a 14

Data Types  Type definitions:  In some languages, we can give new types names.  Example in C: A name given to an int array of ten elements typedef int Array_of_ten_integers[10]; Array_of_ten_integers a; Just like we declare int a, We can declare Array_of_ten_integers a 15

Data Types  Type definitions:  In some languages, we can give new types names.  Example in Ada: type Array_of_ten_integers is array(1..10) of integer; a: Array_of_ten_integers; 16

Data Types  Type definitions:  With new type definitions, we have a problem !  During type checking, a translator must often compare two types to determine if they are the same.  Example: The following should be equivalent in type!  int a[10]  Array_of_ten_integers 17

Data Types  Type definitions:  Each language with type declarations has rules for checking equivalence.  These are called rule equivalence algorithms. 18

Data Types  Strongly Typed Languages:  If a programming language definition specifies a complete type system that can be applied statically.  Guarantees that all (unintentional) data-corrupting errors in a program will be detected at the earliest possible point.  The language is said to be strongly typed. 19

Data Types  Strongly Typed Languages:  All type errors are detected at translation time.  With the exception of a few errors that can only be checked during execution.  E.g. array subscript bounds 20

Data Types  Strongly Typed Languages:  Strong typing ensures that:  Most unsafe programs (programs with data corrupting errors) will be rejected at translation time.  The unsafe programs that are not rejected at translation time will cause an execution error prior to any data corrupting actions.  Strong typing can also be a burden on the programmer, having to explicitly specify types all the time. 21

Data Types  Strongly Typed Languages (Examples): Strong  Ada is a strongly typed language.  ML and Haskell  Pascal Weak  C (Considered weakly typed) 22

Data Types  Untyped Languages:  Languages without static type checking.  Also called dynamically typed languages.  Examples: Scheme, Smalltalk, most scripting languages such as Perl.  An untyped language does not necessarily allow programs to corrupt data, but rather all safety checking is performed at execution time:  They will generate runtime errors. 23

Data Types  Type Construction:  How are new types constructed?  We can think of construction as mathematical operations. 24

Data Types  Type Construction:  Cartesian product.  Unions.  Subsets.  Arrays/Functions.  Sequences/Lists.  Recursive types.  No Intersection: types should NOT overlap. 25

Data Types  Type Construction:  Cartesian product:  Finite combinations of previously defined types.  Example:  C structs: struct IntCharReal { A finite combination of int i; those types char c; double r; }; 26

Data Types  Type Construction:  Unions:  Values belong to ONE of a finite set of types.  Example:  C unions: union IntOrReal Only one of those can { be used. int i; double r; }; 27

Data Types  Type Construction:  Subsets:  A new type may be a subrange of another type.  Example in Ada: subtype IntDigit_Type is integer range 0..9 28

Data Types  Type Construction:  Subsets:  A new type may be a subrange of another type.  Example in Ada: subtype IntDigit_Type is integer range 0..9 29

Data Types  Type Construction:  Arrays and Functions:  You can define arrays in most programming languages as a homogeneous collection of a given type.  You can define functions also. 30

Data Types  Type Construction:  Sequences and Lists:  Sequences are like arrays, except that they are potentially infinite.  Example: Streams  Lists are like arrays, but can only be accessed by counting down from the first element.  Almost all functional languages have lists. 31

Data Types  Type Construction:  Recursive Types:  Example: class node { int value; node next; Recursive Definition } 32

Data Types  Example: Java Type Structure: Java Types Primitive Reference Numeric Array boolean class interface Floating point Integral char float byte double short int long 33

Data Types  Example: C Type Structure: C Types Basic Derived void Numeric Pointer Array Function struct union Integral Floating float double long double (signed) enum (unsigned) char int short int long int 34

Data Types  Type Checking:  Dynamic type checking:  If type information is maintained and checked at runtime.  Interpreters by definition perform dynamic type checking.  Compilers also generate code that maintain type attributes during runtime in table.  Dynamic type checking is required when the types can only be determined at runtime.  Static type checking:  The types are determined from the text of the program.  Checking is performed before execution. 35

Data Types  Type Checking:  A strongly typed language must report all type errors as compilation errors that prevent execution.  A language definition may not specify whether static or dynamic type checking is to be used. 36

Data Types  Type Compatibility:  Two different types may be combined correctly in certain ways.  They are called compatible.  Example: int, short.  What do we mean by combined correctly in a certain way?  Ranges! 37

Data Types  Implicit Types:  Sometimes types are implicitly inferred.  Example: 2 + 3, or x / y  In C, the following is an implicit int variable  X;  Also, in C, if functions have no return type indicated, they implicitly return an int. 38

Data Types  Overlapping Types:  It is sometimes difficult to avoid overlapping of various types.  Example: In Java, the following types overlap:  int  short  long  byte  All overlap 39

Data Types • Shared Operations: – The + operator for example may be applied to any combination of int, short, byte, double, float, long. – The + operator even has a different meaning in: • System.out.println( “ My ” + “ Name ” + “ Is ” ); – The plus operator is said to be overloaded. 40

Data Types  Type Conversion:  There is always a need to convert from one type to another.  Conversion can either be implicit (automatic) or explicit (manual). 41