Object Types, C++ Garbage Collection, Dynamic Memory Protected

Total Page:16

File Type:pdf, Size:1020Kb

Object Types, C++ Garbage Collection, Dynamic Memory Protected a THREE YEARS BACHELOR OF COMPUTER APPLICATION PROGRAMME COURSE CONTENT FOR SEMESTER - III BCA-301 Object Oriented Programming Using G++ Unit - | lntroduction lntroducing Object- Oriented Approach, Relating to other paradigms {Functional, Data decomposition}. Basic terms and Abstractiohr Encapsulation,* lnheritance,.- Polymorphism, I ideas .-Review of C, Difference betweeg C and C++ cin, cout, neY! delete, operators. Unit - ll Classes and Encapsulation, information hiding, abstract data types, Object Objects & ctasses, attributes, methods, C++ class declaration, State identity and behaviour of an object, Constructors and destructors, instantiation of objects, Default parameter value, object types, C++ garbage collection, dynamic memory allocation, Metaclass / abstract classes. Unit- lll lnheritan& and lnheritance, Class hierarchy, derivation - -public, private & Polymorphism protected, Aggregation, composition vs classification hierarchies, Polymorphism, Categorization of polymorphism techniques, Method polymorphism, Polymorphism by I , parameter, Operator overloading, Parametric Polymorphism Unit- IV Generic function Template functioh, function name overloading, Overriding t 7 inheritance methods, Run time polymorphism, Multiplb lnheritance. I Unit - V Files and exception Streams and files, Namespaces, Exception handling, Generic Handling Classes ,I I 2 ,I Referentiat Books: 1. A.R.Venugopal, Rajkumar, T. Ravishanker'Mastering C++", TMH, 1997. t 2. S.B.Lippman & J.Lajoie, " C++ Primer", 3d Edition, Addisoh Wesley, 2000.The C programming Lang., Person Ecl- Dennis Ritchie ' 3. R.Lafore, "Object Oriented Programming using C+*", Ga!$otia Publications, 2004 4. D.Parasons, 'Object Oriented Programming using C+*", BPB Publication. rl e a\ f Using C & L+"+. I lntroduction to Data Representation Structure of single and - and its arrays; sprase murtidimensionat Characteristics Array arrays lower and upper triangurar matrices and Tridiagonal matrices Representation with Vector Unit Il Stacks atso. - and eueues lntroduction and - primitive operations on stack; application; lnfix, postfix, stack preiix Evatuation of postfix expression; conversion"rpr"ssions, and between prefix, infix postfix, introduction and primitive queues, D- queues operation on Unit- lll Lists and priority ql"r"r. lntroductron to tinked tisis; Seq,j;;t"t operations such and tinked tists, as traversat, insertion, deretion Unit- :garghing, Two way rists and Use lV Trees lntroduction of headers and terminotogy; Traversal Recursive of binary trees; algorithms for tr# operations insertion, such as traversat, Unit deretion; Bin ary i"rr.h Tree - V B-Trees lntroduction, The invention o.f B_Tiee; problem; - -searchStatement of the tndexing with binary better trees; a approach t; tree indexes; B-Trees; from the bottom; working__up Unit - Vl Exampre for creating a B-Tree sorting Techniques; tnsertion sort, sLrection sort, sort' heap sort, seat merge .hing Techniqr"r:J tinear search, binary search and hashing - Reftrentiat Books: 1' E'Horowiz and s'sahani, " Fundamentals of Data structures', Gatgotia Book Pvt. Ltd.20O3 source 2' R's'salaria, " Dbta structures & Algorithms,, ,,ru , ArtcflnaKhanna BookuooK lJblishingpblishi 3. Y.Langsam co. (p) Lld..,zoo2 et. Al., 'Data Structures using C and C++,, pHl, lggg a ) 'Basic con"liiuter org anvatlon and design, lnstructions t-.'-?rld instruction codes, Timin0"-- and control/ instruction cycle, Register/ Types"of regist erl general purpose , SPecial purpose regiiters/ & registers, index i Register transfercnJ micro operations/ register i transfer instructioDs, Memory and memory L function, Prtl Data transfer instructiors, Arithmetic logic micro6perations/ shift micro-operatiohs, lnpuU Output and interrupts, Memory instructiors, reference Memory inte*a.ing mem oryl Caohe memory. Unit - ll Central processing General Register organization/ Unit .. stacks organizations instruction iormats, addressing modes, Data transfer and manipuration. program control reduced computer, pipeiine/ RISC/ CISC pipeline processin .ve,g.tor gl array processing. Arithmetic Algorithms: lnteger muttiptication 1lring shift and add, Bogth's algorithm, Integer division, Floating-point representations. Unit- lll Computer Addition, subtraction and multiptication algorithms, I Arithmetic divisor algorithms... Floating point, arithmetic ''] decimal operations, arithmetic operaiions,' decimal arithmetic operations. ) Unit- IV lnput - Output Peripheral devices, lnput/output interfac., ALU l Organization Asynchronous Data ) transfer, mode of transfer, priority interrupts, Direct memory Address (DMA), Inpuu output l-,1 processor (lop), seriat ) communication. Unit V Evaluation of - Overview of lntel 8085 to lntel Pentium processors I Microprocessor microprocessors, Basic ''l J architecture and inierface, internat architecture, extemal architecture I memory and inpuU J output interface. r'l Unit - VI Assembly ranguage, \ Assembter, Assembry rever J instructions, macro, use of macros in ltc instructions, program loops, programming l arithmetic and logic 'l subroutines, Input-outfut programming; 'l Refierential Books: 'l a 1. Leventhat, L.A, 'rntroduction I to Microprocessors,, prentice Hail of rndia 2' Mathur, A.p., "rntroduction to Micropro@ssors,,, Tata McGraw Hiil 3. Rao,P.v.s., 'prospective in computerArchitechture,, , prentice Ha[ of rndia tsCA-304 Bus iness Economics Unit - , The Scope and Scarity & Choice, The Price Mechanism, Demand & Supply Method of Equilibrium: The Concept of Elasticity and it's Applications. Economics, the Economic Problem Output decisions - Revenues Costs and Profit Maximisation -a,2 The Production Process Economics and Diseconomies of scale. Laws of returns & _l Returns to Scale Unit - II Market Equilibrium of a firm and Price, Output Determination under Structure '/ Perfect Competition Monopoly, Monoplastic Competition & Oligopoly Unit- lll Macro€conomic lnflalation, Unemployment, Trade-Cycles, Circular FIow upto Four Concerns Sector Economy, Government in the Macro Economy: Fiscal Policy, Monetary Policy, Measuring national lncome and Output Unit- lV The World WTO, Globalisation, MNC's, Outsourcing, Foreign Capital in Economy lndia, Trips, Groups of Twenty (G-20), lssues of dumping, Expoft- I mport Policy 2004-2009 - Referentia! Books: 1. Ahuja H.L., "Business Economics",S.Chand & Co., New Delhi, 2001 2. Ferfuson P.R., Rothchild, R and Fergusen G.J.'Business Economicls" Mac-millan, I Hampshire, 1993 3. Karl E.Case & Ray C. fair , "Principles of Economics' , Pearson Education , Asia, 2000 4. NelliS, Joseph, Parker David, " The ESSence of BuSineSs Economics",Prentice Hall, New Delhi, 1992. a ) .. ^^. ,i-. l,.r+ population and - uu' U,d. -j't, Definition and scope of statistics, concept of Sample and simple with lllustration, Raw data, attributes and variables, Data classification, frequencY distribution, Cumulative frequency Condensation distribution. Measures of Concept of central Tendency, requirements of a good measures Central of central tendency, Arithmetic mean, Median, Mode, Harmonic Tendency Mean, Geometric mean for grouped and ungrouped data. Unit- III Measures of Concept of dispersion, Absolute and relative measure of Dispersion dispersion, range variance, Standard deviation, Coefficient of variation lV Permutations Permutations of 'n' dissimilar objects taken 'r' at a time (with or Unit- nP, and without repetitions). = n!/(n-r) !(without proof). Combinations nC, Combinations of 'r' objects taken from 'n' objects. = n!/(r!(n-fl!) (without proof) . Simple examples, APplications. Unit - V Sample" Experiments and random experiments, ldeas of deterministic space, and non-deterministic experiments; Definition of sample space, Events and discrete sample Space, events; Types of events, Union and Probability intersections of two or more events, mutually exclusive events, Complementary event, E{haustive event; Simple examples. Classical definition of probability, Addition theorem of probability without Proof (upto three events are expected). Definition of conditional probability Definition of independence of two events, simple numerical problems. -. Unit - VI Statistical lntroduction, control limits, specification limits, tolerance limits, Quality process and product control; Control charts for X and R; Control Control charts for number of defective tn-p chart) ,control charts for number of defects tc - chart) Refierential Books: 1. S.C.Gupta - fundamentals of statistics - sultan chand & sons , Delhi. 2. D.N.Elhance - Fundamentalsof shtistics- Kitab Mahal, Allahabad. -t 3. Montogomery D.C. - StatisticalQuality contro! - John welly and sons 4. Goon, Gupta And Dasgupta- Fundamentals of statistics-The world pressprivate{td., "l Kolkata. 5. Hogg R.V. and Craig R.G. - lntroduction to mathematical statistics Ed 4 {1989} ''l Macmillan Pub. Co. NewYork. ' o.A Gupta S.P. - Statistical Methods , Pub - Sultan Chand and sons New Delhi "] Course Gode Gourse Name "] I BCA-306P Computer Laboratory and Practical Work of OOPS Practicatwillbe based on Paper Object Oriented Programming: Covers UNIT-!|, UNIT-lll, UNIT-IV, UNIT-V of Syllabus BCA-307P Computer Laboratory and Practical Work of DS Practir:alwill be based on Paper Data Struc-ture: Covers UNIT-lll, UNIT-IV, UNIT-V, UNITVI of Syllabus.
Recommended publications
  • Design and Implementation of Generics for the .NET Common Language Runtime
    Design and Implementation of Generics for the .NET Common Language Runtime Andrew Kennedy Don Syme Microsoft Research, Cambridge, U.K. fakeÒÒ¸d×ÝÑeg@ÑicÖÓ×ÓfغcÓÑ Abstract cally through an interface definition language, or IDL) that is nec- essary for language interoperation. The Microsoft .NET Common Language Runtime provides a This paper describes the design and implementation of support shared type system, intermediate language and dynamic execution for parametric polymorphism in the CLR. In its initial release, the environment for the implementation and inter-operation of multiple CLR has no support for polymorphism, an omission shared by the source languages. In this paper we extend it with direct support for JVM. Of course, it is always possible to “compile away” polymor- parametric polymorphism (also known as generics), describing the phism by translation, as has been demonstrated in a number of ex- design through examples written in an extended version of the C# tensions to Java [14, 4, 6, 13, 2, 16] that require no change to the programming language, and explaining aspects of implementation JVM, and in compilers for polymorphic languages that target the by reference to a prototype extension to the runtime. JVM or CLR (MLj [3], Haskell, Eiffel, Mercury). However, such Our design is very expressive, supporting parameterized types, systems inevitably suffer drawbacks of some kind, whether through polymorphic static, instance and virtual methods, “F-bounded” source language restrictions (disallowing primitive type instanti- type parameters, instantiation at pointer and value types, polymor- ations to enable a simple erasure-based translation, as in GJ and phic recursion, and exact run-time types.
    [Show full text]
  • Parametric Polymorphism Parametric Polymorphism
    Parametric Polymorphism Parametric Polymorphism • is a way to make a language more expressive, while still maintaining full static type-safety (every Haskell expression has a type, and types are all checked at compile-time; programs with type errors will not even compile) • using parametric polymorphism, a function or a data type can be written generically so that it can handle values identically without depending on their type • such functions and data types are called generic functions and generic datatypes Polymorphism in Haskell • Two kinds of polymorphism in Haskell – parametric and ad hoc (coming later!) • Both kinds involve type variables, standing for arbitrary types. • Easy to spot because they start with lower case letters • Usually we just use one letter on its own, e.g. a, b, c • When we use a polymorphic function we will usually do so at a specific type, called an instance. The process is called instantiation. Identity function Consider the identity function: id x = x Prelude> :t id id :: a -> a It does not do anything with the input other than returning it, therefore it places no constraints on the input's type. Prelude> :t id id :: a -> a Prelude> id 3 3 Prelude> id "hello" "hello" Prelude> id 'c' 'c' Polymorphic datatypes • The identity function is the simplest possible polymorphic function but not very interesting • Most useful polymorphic functions involve polymorphic types • Notation – write the name(s) of the type variable(s) used to the left of the = symbol Maybe data Maybe a = Nothing | Just a • a is the type variable • When we instantiate a to something, e.g.
    [Show full text]
  • CSE 307: Principles of Programming Languages Classes and Inheritance
    OOP Introduction Type & Subtype Inheritance Overloading and Overriding CSE 307: Principles of Programming Languages Classes and Inheritance R. Sekar 1 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Topics 1. OOP Introduction 3. Inheritance 2. Type & Subtype 4. Overloading and Overriding 2 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Section 1 OOP Introduction 3 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding OOP (Object Oriented Programming) So far the languages that we encountered treat data and computation separately. In OOP, the data and computation are combined into an “object”. 4 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Benefits of OOP more convenient: collects related information together, rather than distributing it. Example: C++ iostream class collects all I/O related operations together into one central place. Contrast with C I/O library, which consists of many distinct functions such as getchar, printf, scanf, sscanf, etc. centralizes and regulates access to data. If there is an error that corrupts object data, we need to look for the error only within its class Contrast with C programs, where access/modification code is distributed throughout the program 5 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Benefits of OOP (Continued) Promotes reuse. by separating interface from implementation. We can replace the implementation of an object without changing client code. Contrast with C, where the implementation of a data structure such as a linked list is integrated into the client code by permitting extension of new objects via inheritance. Inheritance allows a new class to reuse the features of an existing class.
    [Show full text]
  • INF 102 CONCEPTS of PROG. LANGS Type Systems
    INF 102 CONCEPTS OF PROG. LANGS Type Systems Instructors: James Jones Copyright © Instructors. What is a Data Type? • A type is a collection of computational entities that share some common property • Programming languages are designed to help programmers organize computational constructs and use them correctly. Many programming languages organize data and computations into collections called types. • Some examples of types are: o the type Int of integers o the type (Int→Int) of functions from integers to integers Why do we need them? • Consider “untyped” universes: • Bit string in computer memory • λ-expressions in λ calculus • Sets in set theory • “untyped” = there’s only 1 type • Types arise naturally to categorize objects according to patterns of use • E.g. all integer numbers have same set of applicable operations Use of Types • Identifying and preventing meaningless errors in the program o Compile-time checking o Run-time checking • Program Organization and documentation o Separate types for separate concepts o Indicates intended use declared identifiers • Supports Optimization o Short integers require fewer bits o Access record component by known offset Type Errors • A type error occurs when a computational entity, such as a function or a data value, is used in a manner that is inconsistent with the concept it represents • Languages represent values as sequences of bits. A "type error" occurs when a bit sequence written for one type is used as a bit sequence for another type • A simple example can be assigning a string to an integer
    [Show full text]
  • Parametric Polymorphism (Generics) 30 April 2018 Lecturer: Andrew Myers
    CS 4120/5120 Lecture 36 Parametric Polymorphism (Generics) 30 April 2018 Lecturer: Andrew Myers Parametric polymorphism, also known as generics, is a programming language feature with implications for compiler design. The word polymorphism in general means that a value or other entity that can have more than one type. We have already talked about subtype polymorphism, in which a value of one type can act like another (super)type. Subtyping places constraints on how we implemented objects. In parametric polymorphism, the ability for something to have more than one “shape” is by introducing a type parameter. A typical motivation for parametric polymorphism is to support generic collection libraries such as the Java Collection Framework. Prior to the introduction of parametric polymorphism, all that code could know about the contents of a Set or Map was that it contained Objects. This led to code that was clumsy and not type-safe. With parametric polymorphism, we can apply a parameterized type such as Map to particular types: a Map String, Point maps Strings to Points. We can also parameterize procedures (functions, methods) withh respect to types.i Using Xi-like syntax, we might write a is_member method that can look up elements in a map: contains K, V (c: Map K, V , k: K): Value { ... } Map K, V h m i h i ...h i p: Point = contains String, Point (m, "Hello") h i Although Map is sometimes called a parameterized type or a generic type , it isn’t really a type; it is a type-level function that maps a pair of types to a new type.
    [Show full text]
  • Polymorphism
    Polymorphism A closer look at types.... Chap 8 polymorphism º comes from Greek meaning ‘many forms’ In programming: Def: A function or operator is polymorphic if it has at least two possible types. Polymorphism i) OverloaDing Def: An overloaDeD function name or operator is one that has at least two Definitions, all of Different types. Example: In Java the ‘+’ operator is overloaDeD. String s = “abc” + “def”; +: String * String ® String int i = 3 + 5; +: int * int ® int Polymorphism Example: Java allows user DefineD polymorphism with overloaDeD function names. bool f (char a, char b) { return a == b; f : char * char ® bool } bool f (int a, int b) { f : int * int ® bool return a == b; } Note: ML Does not allow function overloaDing Polymorphism ii) Parameter Coercion Def: An implicit type conversion is calleD a coercion. Coercions usually exploit the type-subtype relationship because a wiDening type conversion from subtype to supertype is always DeemeD safe ® a compiler can insert these automatically ® type coercions. Example: type coercion in Java Double x; x = 2; the value 2 is coerceD from int to Double by the compiler Polymorphism Parameter coercion is an implicit type conversion on parameters. Parameter coercion makes writing programs easier – one function can be applieD to many subtypes. Example: Java voiD f (Double a) { ... } int Ì double float Ì double short Ì double all legal types that can be passeD to function ‘f’. byte Ì double char Ì double Note: ML Does not perform type coercion (ML has no notion of subtype). Polymorphism iii) Parametric Polymorphism Def: A function exhibits parametric polymorphism if it has a type that contains one or more type variables.
    [Show full text]
  • Obstacl: a Language with Objects, Subtyping, and Classes
    OBSTACL: A LANGUAGE WITH OBJECTS, SUBTYPING, AND CLASSES A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY By Amit Jayant Patel December 2001 c Copyright 2002 by Amit Jayant Patel All Rights Reserved ii I certify that I have read this dissertation and that in my opin- ion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. John Mitchell (Principal Adviser) I certify that I have read this dissertation and that in my opin- ion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Kathleen Fisher I certify that I have read this dissertation and that in my opin- ion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. David Dill Approved for the University Committee on Graduate Studies: iii Abstract Widely used object-oriented programming languages such as C++ and Java support soft- ware engineering practices but do not have a clean theoretical foundation. On the other hand, most research languages with well-developed foundations are not designed to support software engineering practices. This thesis bridges the gap by presenting OBSTACL, an object-oriented extension of ML with a sound theoretical basis and features that lend themselves to efficient implementation. OBSTACL supports modular programming techniques with objects, classes, structural subtyping, and a modular object construction system. OBSTACL's parameterized inheritance mechanism can be used to express both single inheritance and most common uses of multiple inheritance.
    [Show full text]
  • Polymorphism
    Chapter 4 Polymorphism Previously, we developed two data structures that implemented the list abstract data type: linked lists and array lists. However, these implementations were unsatisfying along two dimensions: 1. Even though a linked list and array list provide the same functions to a user, they are not interchangable in user code. If the user wanted the flexibility of choosing between a linked list or array list (because they have different performance characteristics), they would need to duplicate code who’s only difference would be the type of the list it uses. 2. The carrier type of either list implementation is fixed. If we wanted a list that held integers as well as a list that contained strings, we would need two separate implementations of the list that only differed in the type of the value at each node. To solve both these problems, Java provides us mechanisms to write polymorphic code. Polymorphic code is code that can operate over multiple types. In particular, the two problems described above are addressed with two types of polymorphisms: subtype polymorphism and parametric polymorphism. 4.1 Subtype Polymorphism The list abstract data type defined a number of operations that all “list-like” objects ought toimplement: • int size(), • void add(int x), • void insert(int x, int index), • void clear(), • int get(int index), and • int remove(int index). Our linked list and array list classes implemented these methods. However, there was no enforcement by the compiler that these classes actually implemented these operations. Furthermore, even though the two list implementations provided the exact same set of methods to the user, we could not interchange one list for another because they are different types.
    [Show full text]
  • First-Class Type Classes
    First-Class Type Classes Matthieu Sozeau1 and Nicolas Oury2 1 Univ. Paris Sud, CNRS, Laboratoire LRI, UMR 8623, Orsay, F-91405 INRIA Saclay, ProVal, Parc Orsay Universit´e, F-91893 [email protected] 2 University of Nottingham [email protected] Abstract. Type Classes have met a large success in Haskell and Is- abelle, as a solution for sharing notations by overloading and for spec- ifying with abstract structures by quantification on contexts. However, both systems are limited by second-class implementations of these con- structs, and these limitations are only overcomed by ad-hoc extensions to the respective systems. We propose an embedding of type classes into a dependent type theory that is first-class and supports some of the most popular extensions right away. The implementation is correspond- ingly cheap, general and integrates well inside the system, as we have experimented in Coq. We show how it can be used to help structured programming and proving by way of examples. 1 Introduction Since its introduction in programming languages [1], overloading has met an important success and is one of the core features of object–oriented languages. Overloading allows to use a common name for different objects which are in- stances of the same type schema and to automatically select an instance given a particular type. In the functional programming community, overloading has mainly been introduced by way of type classes, making ad-hoc polymorphism less ad hoc [17]. A type class is a set of functions specified for a parametric type but defined only for some types.
    [Show full text]
  • Lecture Notes on Polymorphism
    Lecture Notes on Polymorphism 15-411: Compiler Design Frank Pfenning Lecture 24 November 14, 2013 1 Introduction Polymorphism in programming languages refers to the possibility that a function or data structure can accommodate data of different types. There are two principal forms of polymorphism: ad hoc polymorphism and parametric polymorphism. Ad hoc polymorphism allows a function to compute differently, based on the type of the argument. Parametric polymorphism means that a function behaves uniformly across the various types [Rey74]. In C0, the equality == and disequality != operators are ad hoc polymorphic: they can be applied to small types (int, bool, τ∗, τ[], and also char, which we don’t have in L4), and they behave differently at different types (32 bit vs 64 bit compar- isons). A common example from other languages are arithmetic operators so that e1 + e2 could be addition of integers or floating point numbers or even concate- nation of strings. Type checking should resolve the ambiguities and translate the expression to the correct internal form. The language extension of void∗ we discussed in Assignment 4 is a (somewhat borderline) example of parametric polymorphism, as long as we do not add a con- struct hastype(τ; e) or eqtype(e1; e2) into the language and as long as the execution does not raise a dynamic tag exception. It should therefore be considered some- what borderline parametric, since implementations must treat it uniformly but a dynamic tag error depends on the run-time type of a polymorphic value. Generally, whether polymorphism is parametric depends on all the details of the language definition.
    [Show full text]
  • Lecture 5. Data Types and Type Classes Functional Programming
    Lecture 5. Data types and type classes Functional Programming [Faculty of Science Information and Computing Sciences] 0 I function call and return as only control-flow primitive I no loops, break, continue, goto I instead: higher-order functions! Goal of typed purely functional programming: programs that are easy to reason about So far: I data-flow only through function arguments and return values I no hidden data-flow through mutable variables/state I instead: tuples! [Faculty of Science Information and Computing Sciences] 1 Goal of typed purely functional programming: programs that are easy to reason about So far: I data-flow only through function arguments and return values I no hidden data-flow through mutable variables/state I instead: tuples! I function call and return as only control-flow primitive I no loops, break, continue, goto I instead: higher-order functions! [Faculty of Science Information and Computing Sciences] 1 I high-level declarative data structures I no explicit reference-based data structures I instead: (immutable) algebraic data types! Goal of typed purely functional programming: programs that are easy to reason about Today: I (almost) unique types I no inheritance hell I instead of classes + inheritance: variant types! I (almost): type classes [Faculty of Science Information and Computing Sciences] 2 Goal of typed purely functional programming: programs that are easy to reason about Today: I (almost) unique types I no inheritance hell I instead of classes + inheritance: variant types! I (almost): type classes
    [Show full text]
  • Type Systems, Type Inference, and Polymorphism
    6 Type Systems, Type Inference, and Polymorphism Programming involves a wide range of computational constructs, such as data struc- tures, functions, objects, communication channels, and threads of control. Because programming languages are designed to help programmers organize computational constructs and use them correctly, many programming languages organize data and computations into collections called types. In this chapter, we look at the reasons for using types in programming languages, methods for type checking, and some typing issues such as polymorphism and type equality. A large section of this chap- ter is devoted to type inference, the process of determining the types of expressions based on the known types of some symbols that appear in them. Type inference is a generalization of type checking, with many characteristics in common, and a representative example of the kind of algorithms that are used in compilers and programming environments to determine properties of programs. Type inference also provides an introduction to polymorphism, which allows a single expression to have many types. 6.1 TYPES IN PROGRAMMING In general, a type is a collection of computational entities that share some common property. Some examples of types are the type Int of integers, the type Int!Int of functions from integers to integers, and the Pascal subrange type [1 .. 100] of integers between 1 and 100. In concurrent ML there is the type Chan Int of communication channels carrying integer values and, in Java, a hierarchy of types of exceptions. There are three main uses of types in programming languages: naming and organizing concepts, making sure that bit sequences in computer memory are interpreted consistently, providing information to the compiler about data manipulated by the program.
    [Show full text]