Software Engineering
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Software Engineering. Dr Ian Reid B4, 4 lectures, Hilary Term. Software Engineering vs structured programming. Not really a course about software engineering… Software engineering Mostly about concepts, Structured programming

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Software Engineering

  • Dr Ian Reid

  • B4, 4 lectures, Hilary Term

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Software Engineering vs structured programming

  • Not really a course about software engineering…

  • Software engineering

    • Mostly about concepts,

  • Structured programming

    • Revision, coding in C and Matlab, functions

  • Data structures

    • structures, classes

  • Object oriented programming

    • objects, object-oriented concepts like inheritance, polymorphism, patterns and the standard template library

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Learning Outcomes

  • The course will aim to give a good understanding of basic design methods, and emphasize the need to produce well-structured maintainable computer software. The course will concentrate on principles, but these will be reinforced with examples in Matlab and C/C++ programming languages. Specifically, by the end of the course students should:

    • understand concepts of basic program design techniques that can be applied to a variety of programming languages, in particular Matlab and C/C++

    • understand the need for structured programming in software projects

    • be able to recognise and to produce and/or maintain well structured programs

    • have a basic understanding of the role of and advantages of object oriented design

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  • Sommerville, Software Engineering, Addison-Wesley (8th edition), 2007.

  • Wirth, Algorithms + Data Structures = Programs, Prentice-Hall, 1975

  • Leveson, Safeware: System Safety and Computers, Addison-Wesley, 1995.

  • Lipmann and Lajoie, C++ Primer, Addison-Wesley, 2005.

  • Goodrich et al., Data structures and algorithms in C++, Wiley, 2004

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The Role of Computing in Engineering

  • Computing is ubiquitous in engineering. Why?

  • Awesome speed of modern, everyday computers a makes complicated analysis and simulation possible across all domains.

  • Applications in design and modelling. Far beyond the reach of the mortal human engineer. Indeed many modelling problems are utterly infeasible without modern computers and software.

  • In embedded systems, computers can provide a level of power, speed, flexibility and control not otherwise possible (eg mobile phone)

  • Computing is “cheap” (but exercise this argument with care)

  • Software is the key…

some examples…

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Example: mobile phone

  • Even simple mobile phones rely on software

  • Typical phone has a microcontroller (SIM card) with a small program

    • Drive GUI

    • Control devices (keypad, microphone, a/d, dsp, decoder)

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Example: Sizewell B

  • Nuclear power station (PWR), onstream in 1995

  • Software used extensively in the design

  • Software for control!

    • first UK reactor to use software in its Primary Protection System)

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Example: A380

  • A380

  • 1400 separate programs

  • There is a software project just to manage all the software!

  • Clearly safety-critical features of the software

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Example: NPfIT

  • NHS National Plan for IT

  • Plan to provide electronic care records for patients

  • Connect 30000 GPs and 300 hospitals

  • Provide secure access to records for healthcare professionals

  • Provide access for patients to their own records via “Healthspace”

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Software engineering versus programming

  • Software engineering is about more than just programming/coding

  • It is about design principles and methodologies that yield programs that are

    • Robust

    • Manageable

    • Reusable

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Software vs “other” engineering

  • How is software engineering similar to other engineering?

  • Abstraction and Modularity

    • Consider free-body diagram

    • Thevenin/Norton

    • Low output impedance / High input impedance

    • Digital computer

  • We return to these concepts later…

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Modularity: Op-amp buffer

  • Unity gain buffer

  • Vout = Vin

  • Very high input impedance, very low output impedance

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Software vs “other” engineering

  • How is software different to other engineering?

  • Pure, weightless, flexible

  • Capacity to incorporate massive complexity

  • No manufacturing defects, corrosion, aging

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Intrinsic difficulties with software

  • Analogue versus discrete state systems

  • The “curse” of flexibility

    • Can encourage unnecessary complexity

    • Redefinition of tasks late in development – shifting goal-post

  • Complexity and invisible interfaces

    • Standard way of dealing with complexity is via modularity

    • But this alone is not enough because interfaces can be subtle and invisible, and here too there is a need to control complexity

  • Historical usage information

    • Unlike physical systems, there is a limited amount of experience about standard designs

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When software projects go wrong

  • A320, Habsheim and Strasbourg

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When software projects go wrong

  • London Ambulance Service

    • 1992, computerised ambulance despatch system fails

  • Therac-25

    • 2 people died and several others exposed to dangerous levels of radiation because of software flaws in radiotherapy device


    • £5M University financial package

    • Expenditure to date more like £20-25M

  • NPfIT?

    • NHS £12 billion IT project

  • comp.risks is a great source of others...

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NHS National programme for IT: NPfIT

  • Plan to provide electronic care records for patients

  • Connect 30000 GPs and 300 hospitals

  • Provide secure access to records for healthcare professionals

  • Provide access for patients to their own records via “Healthspace”

  • Laudable?

  • Realistic?

    • Software Engineering specialists have their doubts

    • Ross Anderson (Prof of Security Engineering, Cambridge Computing Laboratory) wrtes in his blog “I fear the whole project will just continue on its slow slide towards becoming the biggest IT disaster ever”.

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Software life-cycle

  • Software development stages

    • Specification

    • Design

    • Implementation

    • Integration

    • Validation

    • Operation/Maintenance/Evolution

  • Different types of system organise these generic activities in different ways

  • Waterfall approach treats them as distinct stages to be signed off chronologically

  • In practice usually an iteration of various steps

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  • Vague initial goals

  • Iterative refinement

  • Leading to more precise specification

  • Example

    • Calculate the n-bounce trajectory of a lossy bouncing ball.

    • Refine this to consider

      • What does the statement actually mean?

      • Physics

      • Initial conditions

      • Air-resistance?

      • Stopping criterion (criteria)?

    • Now, think about how to design/implement

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  • Verification: does the system confirm to spec?

  • Validation: does it actually do what it was supposed to?

  • Top-down vs bottom-up testing

  • Black-box vs white-box testing

  • Impossibility of exhaustive testing

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Extreme programming (XP)

  • Proposed in the late 90s as a reaction to problems with “traditional” development processes

  • Takes extreme position compared with waterfall approach

  • Appropriate for small-medium sized projects

    • Teams of pairs of programmer, programming together

    • Incremental development, frequent system releases

    • Code constantly refined, improved, made as simple as possible

    • Do not design for change; instead change reactively

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Top down design

  • Here want to keep in mind the general principles

    • Abstraction

    • Modularity

  • Architectural design: identifying the building blocks

  • Abstract specification: describe the data/functions and their constraints

  • Interfaces: define how the modules fit together

  • Component design: recursively design each block

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Modular design


Data structures


  • Procedural programming: focus on algorithms

  • Object-oriented programming: focus on data structures

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Structured programming

  • Top-down vs bottom-up

  • Both are useful as a means to understand the relations between high-level and low-level views of a program

  • Top-down

    • Code high level parts using “stubs” with assumed functionality for low-level dependencies

    • Iteratively descend to lower-level modules

  • Bottom-up

    • Code and test each low-level component

    • Need “test harness” so that low-level can be tested in its correct context

    • Integrate components

  • Not hard-fast rules; combination often best

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Simple design tools

  • Flow chart

  • Pseudo-code

    • Wait for alarm

    • Count = 1

    • While (not ready to get up and count <= 3)

      • Hit snooze button

      • Increment count

    • Climb out of bed

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Data flows

  • Data flow diagram

  • Simple example, VTOL simulator

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Simple design tools

  • State diagram

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Basic coding techniques

  • Pretty much any program can be specified using:

    • Sequences of instructions

      • { Do A; Do B; Do C }

    • Conditional instructions

      • If (condition) Do A

    • Repetitions (loops)

      • While (condition) Do A

  • These semantic concepts are implemented in different high-level programming languages using different syntax

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Implementation in Matlab and C

N= 10;

tot = 0;

totsq = 0;

for i=1:N

tot = tot+i;

totsq = totsq+i^2;




int i;

int tot = 0;

int totsq = 0;

for (i=1; i<N; i++) {

tot += i;

totsq += i*i;


cout << tot << endl;

cout << totsq << endl;

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Notes on coding style

  • Use meaningful variable names

  • Use comments to supplement the meaning

  • Indent code for each block/loop

  • Encapsulate groups of statements sensibly in functions

  • Encapsulate related data sensibly in data structures

  • Design top down

  • Code bottom-up or top-down, or a combination

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Matlab vs C

  • Matlab and C are both procedural languages

  • Matlab is an interpreted language

    • each statement decoded and executed in turn

  • C is a compiled language

    • each module (.c file) is converted into assembly language

    • The interfaces between the modules are

      • Shared global data

      • Function calls from one module to another

    • This is resolved at link time when the modules are linked together into an executable

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Procedural programming

  • Aim is to break program down into functional units

    • procedures or functions

    • Set of inputs, set of outputs

  • In Matlab and C this procedural building block is the function

  • Understanding functions…

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Organisation of Matlab programs

  • A Matlab “program” may be a script or function

    • i.e. a sequence of instructions

  • This script or function will typically call a bunch of other functions

  • Functions are stored in .m files

  • Multiple functions can be stored in one .m file, but only first is visible outside

    • The others are local functions

    • Part of the recursive subdivision of the problem

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Matlab file organisation





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Organisation of C programs

Source code

Source code


.c .cc

.c .cc



Object file

Object file






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  • Function definition

  • Function call

  • Function prototype

  • Scope (local versus global data)

  • Parameters and return value(s)

  • Function call

  • Low-level implementation of function calls

  • Recursion

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Function definition

% compute factorial

function z = fact(n)

% function body

z = 1;

for i=1:n

z = z*i;


// compute factorial

int fact(int n)


int i, val = 1;

for (i=1; i<=n; i++) {

val *= i;


return val;


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Function call

  • Distinguish between

    • The function definition

      • Defines the set of operations that will be executed when the function is called

      • The inputs

      • The outputs

    • And the function call

      • i.e. actually using the function

  • Formal vs Actual parameters

  • Return value(s)

    • The value of a function evaluation is the return value


a = 6;

z = fact(a);

[V,D] = eig(A);

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Function prototype

  • The function prototype provides enough information to the compiler so that it can check that it is being called correctly

  • Defines the interface

    • Input (parameter), output (return value)

myexp.h file

float myexp(float x);

myexp.c file

float myexp(float x)


const float precision = 1.0e-6;

float term=1.0, res=0.0;

int i=0;

while (fabs(term)>precision) {

res += term;


term = pow(x,i)/fact(i);


return res;


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Scope: local variables

  • Variables which are declared inside a function are local variables

  • They cannot be “seen” outside the function (block) in which they are declared

  • A local variable exists only for the duration of the current function execution

  • It is declared as a new variable every time the function is called

  • It ceases to exist when the function returns

  • It does not “remember” its value between calls

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Scope: global variables

  • Global variables exist outside all functions

  • A global variable is visible inside functions

  • If there exist two variables, one local, one global, with the same name, then the local one takes precedence within its local scope

  • C and Matlab behave differently

    • C will use a global if no local exists

    • Matlab only uses a global if the programmer explicitly requests it

  • Globals should be used with caution because their use inside a function compromises its encapsulation

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  • Want the function to behave in the same way for the same inputs

    • encapsulate particular functional relationship

  • But if the function depends on a global it could behave differently for the same inputs

  • Live example using myexp

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Output values

Input parameters

Hidden input

Output values

Input parameters

Hidden output

Function encapsulation

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  • Could set value of a global variable in a function

  • Again this compromises the function’s encapsulation

    • Causes a side-effect

    • An implicit output, not captured by the interface

  • Makes it difficult to re-use code with confidence

  • c.f. C and Matlab function libraries

    • Set of re-usable routines with well defined interfaces

  • In small projects maybe not a big problem

  • Hugely problematic in bigger projects, especially when multiple programmers working as a team

  • Complicates interfaces between components, possibly in unintended ways

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Low-level implementation of function call



machine code


global variables

local variable m

local variable 1

return location

parameter x



parameter 1

return value n


return value 1

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Pass by value/reference

int i=5, j=10;


cout << i << “ “ << j << endl;

Pass by value

Pass by reference

void swap(int a, int b)


int temp = a;

a = b;

b = temp;



void swap(int& a, int& b)


int temp = a;

a = b;

b = temp;



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  • Recursion is the programming analogue of induction:

    • If p(0) and p(n) implies p(n+1)

    • Then p(n) for all n

  • Define a function in terms of

    • Itself

    • Boundary conditions

  • For example

    • Factorial: n! = n * (n-1)!, 0! = 1

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Data types and data structures

  • C/C++ predefine a set of atomic types

    • bool, char, int, float, double

  • C/C++ provides machanism for building compound data structures

    • struct (class)

    • Array

  • Matlab supports arrays/matrices (of course)

  • Matlab also supports structures

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C/C++: struct and class

  • A class (struct in C) is a compound data type which encapsulates related data into a single entity

    class Complex {


    double re, im;


  • Defines how a variable of this type will look

    int i;

    Complex z;

Class definition

Create a variable (an instance) of this type

Example vtol state l.jpg







Example: VTOL state

  • Represent current state as, say, a triple of numbers and a bool, (position, velocity, mass, landed)

  • Single variable represents all numbers

    • Better abstraction!

      class State {

      double pos, vel, mass;

      bool landed;


      State s;

Accessing class members l.jpg

State s;

s.pos = 1.0;

s.vel = -20.0;

s.mass = 1000.0;

s.landed = false;

s.pos = s.pos + s.vel*deltat;

Thrust = ComputeThrust(s);

In Matlab introduce structure fields without declaration

s.pos = 1.0;

s.vel = -20.0;

Thrust = ComputeThrust(s);

Accessing class members

Output parameters l.jpg
Output parameters

Image ReadImage(const string filename, bool& flag);

bool ReadImage(const string filename, Image& im);

  • Input: filename (type string)

  • Output:

    • im (type Image)

    • boolean flag indicating success/failure

      function [Image, errflag] = ReadImage(filename)

  • Basically the same, but cleaner in Matlab!

Arrays l.jpg

  • An array is a data structure containing a numbered (indexed) collection of items of a single data type

    int a[10];

    res = a[0] + a[1] + a[2];

    Complex z[20];

    State s[100];

    for (t=1; t<100; t++) {

    s[t].pos = s[t-1].pos + s[t-1].vel + 0.5*g;

    s[t].vel = s[t-1].vel + g – GetThrust(s[t-1], burnrate)/s[t-1].mass;

    s[t].mass = s[t-1].mass – burnrate*escapevel;


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Multi-dimensional arrays

double d[10][5];

has elements:

d[0][0] d[0][1] … d[0][4]




d[9][0] d[9][1] … d[9][4]

Methods l.jpg

  • In C++ a class encapsulates related data and functions

  • A class has both data fields and functions that operate on the data

  • A class member function is called a method in the object-oriented programming literature

Example l.jpg

class Complex {


double re, im;

double Mag() {

return sqrt(re*re + im*im);


double Phase() {

return atan2(im, re);



Complex z;

cout << “Magnitude=“ << z.Mag() << endl;

Constructor l.jpg

  • Whenever a variable is created (declared), memory space is allocated for it

  • It might be initialised

    • int i;

    • int i=10;

    • int i(10);

  • In general this is the work of a constructor

  • The constructor is a special function with the same name as the class and no return type

    • Complex(double x, double y) {

      { re = x; im = y; }

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Information hiding / encapsulation

  • Principle of encapsulation is that software components hide the internal details of their implementation

  • In procedural programming, treat a function as black boxes with a well-defined interface

    • Need to avoid side-effects

    • Use these functions as building blocks to create programs

  • In object-oriented programming, a class defines a black box data structure, which has

    • Public interface

    • Private data

  • Other software components in the program can only access class through well-defined interface, minimising side-effects

Example61 l.jpg

class Complex {


Complex(double x, double y) { re=x; im=y; }

double Re() { return re; }

double Im() { return im; }

double Mag() { return sqrt(re*re + im*im);}

double Phase() { return atan2(im, re);



double re, im;


Complex z(10.0,8.0);

cout << “Magnitude=“ << z.Mag() << endl;

cout << “Real part=“ << z.Re() << endl;

Example62 l.jpg

class Complex {


Complex(double x, double y) {

r = sqrt(x*x + y*y);

theta = atan2(y,x);


double Re() { return r*cos(theta); }

double Im() { return r*sin(theta); }

double Mag() { return r;}

double Phase() { return theta; }



double r, theta;


Complex z(10.0,8.0);

cout << “Magnitude=“ << z.Mag() << endl;

cout << “Real part=“ << z.Re() << endl;

C program organisation l.jpg


class Complex {


Complex(double x, double y);

double Re();

double Im();

double Mag();

double Phase();


double re, im;


C++ program organisation

C program organisation64 l.jpg


#include “Complex.h”

Complex::Complex(double x, double y) {

re = x; im = y;


double Complex::Re() { return re; }

double Complex::Im() { return im; }

double Complex::Mag() {

return sqrt(re*re+im*im);


double Complex::Phase() { return atan2(im,re); }

C++ program organisation

Object oriented programming l.jpg
Object-oriented programming

  • An object in a programming context is an instance of a class

  • Object-oriented programming concerns itself primarily with the design of classes and the interfaces between these classes

  • The design stage breaks the problem down into classes and their interfaces

  • OOP also includes two important ideas concerned with hierarchies of objects

    • Inheritance

    • polymorphism

Inheritance l.jpg

  • Hierarchical relationships often arise between classes

  • Object-oriented design supports this through inheritance

  • An derived class is one that has the functionality of its “parent” class but with some extra data or methods

  • In C++

    class A : public B {


Example67 l.jpg

class Window

Data: width, height

posx, posy

Methods: raise(), hide()

select(), iconify()

class TextWindow

class GraphicsWindow

Data: cursor_x, cursor_y

Data: background_colour

Methods: redraw(), clear()

backspace(), delete()

Methods: redraw(), clear()


class InteractiveGraphicsWindow


Methods: MouseClick(), MouseDrag()


Polymorphism l.jpg

  • Polymorphism, Greek for “many forms”

  • One of the most powerful object-oriented concepts

  • Ability to hide alternative implementations behind a common interface

  • Ability of objects of different types to respond in different ways to a similar event

  • Example

    • TextWindow and GraphicsWindow, redraw()

Implementation l.jpg

  • In C++ run-time polymorphism implemented via virtual functions

    class Window {

    virtual void redraw();


Example70 l.jpg

  • Class A is base class, B and C both inherit from A

  • If the object is of type A then call A’s func()

  • If the object is of type B then call B’s func()

  • If the object is of type C then call C’s func()

  • If class A defines func() as

    virtual void func() = 0;

    then A has no implementation of func()

  • class A is then an abstract base class

    • It is not possible to create an instance of class A, only instances derived classes, B and C

    • class A defines an interface to which all derived classes must conform

  • Use this idea in designing program components

    • Specify interface, then have a guarantee of compatibility of all derived objects

Another example l.jpg
Another example

  • Consider a vector graphics drawing package

  • Consider base class “Drawable”

    • A graphics object that knows how to draw itself on the screen

    • Class hierarchy may comprise lines, curves, points, images, etc

  • Program keeps a list of objects that have been created and on redraw, displays them one by one

  • This is implemented easily by a loop

    for (int i=0; i<N; i++) {



Templates l.jpg

  • Templating is a mechanism in C++ to create classes in which one or more types are parameterised

  • Example of compile-time polymnorphism

    class BoundedArray {


    float GetElement(int i) {

    if (i<0 || i>=10) {

    cerr << “Access out of bounds\n”;

    return 0.0;

    } else {

    return a[i];




    float a[10];


Templates73 l.jpg

template <class Type>

class BoundedArray {


Type GetElement(int i) {

if (i<0 || i>=10) {

cerr << “Access out of bounds\n”;

return Type(0);

} else {

return a[i];




Type a[10];


BoundedArray<int> x;

BoundedArray<Complex> z;

Design patterns l.jpg
Design patterns

  • Programs regularly employ similar design solutions

  • Idea is to standardise the way these are implemented

    • Code re-use

    • Increased reliability

    • Fewer errors, shorter development time

  • An array is special case of a container type

    • Way of storing a collection of possibly ordered elements.

    • List, stack, queue, double-ended list, etc

  • Templates in C++ offer a way of providing libraries to implement these standard containers

Standard template library l.jpg
Standard Template Library

  • C++ provides a set of container classes

    • Standard way of representing and manipulating container types

    • eg, methods insert(), append(), size(), etc

  • STL supports

    • Stack (FILO structure)

    • List (efficient insertion and deletion, ordered but not indexed)

    • Vector (extendible array)

    • others

Stl example l.jpg
STL example

  • std::vector<Type> is an extendible array

  • It can increase its size as the program needs it to

  • It can be accessed like an ordinary array (eg v[2])

  • It can report its current size

    • v.size()

  • You can add an item to the end without needing to know how big it is

    • v.push_back(x)


int main() {

std::vector<int> v;

for (int i=0; i<20; i++) v.push_back(i);

for (int i=0; i<v.size(); i++)

std::cout << v[i] << std::endl;


Stl continued l.jpg
STL, continued

  • To create a new STL vector of a size specified at run-time

    int size;

    std::vector<Complex> z;

    std::cin >> size;


    z[5] = Complex(2.0,3.0);

Stl continued78 l.jpg
STL, continued

  • To create a two dimensional array at run-time

    int width, height;

    std::vector< std::vector<int> > x;


    for (int i=0; i<height; i++)


    x[2][3] = 10;

Iterators l.jpg

  • A standard thing to want to do with a collection of data elements is to iterate over each

    • for (int i=0; i<v.size(); i++)

  • Not all container types support indexing

    • A linked list has order, but only relative order

  • An iterator is a class that supports the standard programming pattern of iterating over a container type

    std::vector<int> v;

    std::vector<int>::iterator i;

    for (it=v.begin(); it!=v.end(); it++) …

  • An iterator encapsulates the internal structure of how the iteration occurs

Complete example l.jpg
Complete example

  • Design a program to compute a maze

    • User-specified size

    • Print it out at the end

  • Algorithm

    • Mark all cells unvisited

    • Choose a start cell

    • While current cell has unvisited neighbours

      • Choose one at random

      • Break wall between it and current cell

      • Recursively enter the chosen cell

Design data structures l.jpg
Design data structures

  • Maze class

    • Compute method

    • Print method

    • Two dimensional array of Cells

  • Cell class

    • Accessor methods

    • Break wall methods

    • Wall flags

    • Visited flag

Cell class interface l.jpg
Cell class interface

class Cell {



bool Visited();

void MarkVisited();

bool BottomWall();

bool RightWall();

void BreakBottom();

void BreakRight();


bool bottomwall;

bool rightwall;

bool visited;


Maze class interface l.jpg
Maze class interface

class Maze {


Maze(int width, int height);

void Compute(int x, int y);

void Print();


int Rand(int n);

int H, W;

std::vector< std::vector<Cell> > cells;


Main program l.jpg
Main program

int main(int argc, char* argv[])


int width, height;

cerr << "Enter maze width: ";

cin >> width;

cerr << "Enter maze height: ";

cin >> height;

Maze m(width, height);



return 0;


Concept summary l.jpg
Concept summary

  • Top-down design

    • Abstraction

    • Encapsulation / information hiding

    • Modularity

  • Functions

  • Classes / objects

  • Inheritance

  • Polymorphism

  • Templates

  • Patterns

  • Exam questions? See tute sheet.