General description
In the 40 or so years that people have been developing software, a range of common tasks, such as sorting and searching, have been identified and a body of knowledge has been accumulated about how these tasks are best carried out. This knowledge usually consists of various ways of structuring the data, a range of different algorithms, and performance analysis that enables a designer to chose the algorithm and data structure most appropriate to the circumstances of the system, and to predict how the system will perform.

This module introduces students to this body of knowledge and sets it into the context of modern software design and structuring techniques.

Aims
The aims of this module are to:

• introduce students to the standard data structures and algorithms that form the common currency of software design;
• develop students' understanding of the object-oriented model of software;
• introduce students to the analysis and prediction of performance.

Learning outcomes
On successful completion of the module, students should:

• be able to demonstrate an understanding of the concepts of problem abstraction and program design;
• be capable of realising their designs in software that is robust, reusable and maintainable;
• apply the principles of abstractions and encapsulation, to their object-oriented design;
• understand the importance of the ideas of space and time complexity and be able to use them to predict the behaviour of simple systems;
• be able to use a variety of approaches to algorithm design such as greedy algorithms, divide and conquer and dynamic programming;
• be able to evaluate and choose appropriate data structures and algorithms for a range of programming problems;
• have an appreciation of the importance of program correctness and some of the techniques used to ensure it.

Syllabus
1. Course overview - 4 Lectures
An introduction into time/space complexity. Issues of correctness as they relate to the definition of ADTs. The key ideas of abstraction and encapsulation. Notations for describing ADTs.

2. Further design and implementation of computer programs - 12 Lectures
Programming language facilities will be investigated to support more advanced data structures and the development of larger, more reusable software. An introduction to linear data structures: stacks, queues and lists of objects, implemented using arrays and utilising interfaces. An introduction to recursion.

3. Dynamic storage allocation - 8 Lectures
An investigation into unbounded data structures and a comparison with their bounded equivalents.

4. Object-oriented design - 1 Lecture
An introduction to object-oriented analysis and design. A brief consideration of use case analysis.

5. The unified modelling language (UML) - 2 Lectures
The UML is an attempt by Booch, Rumbaugh and Jacobson to produce a common language for describing OO designs. These lectures will provide a brief introduction to UML.

6. The object modelling technique (OMT) - 6 Lectures
The OMT approach to object-oriented design will be considered and the analysis phase will be discussed. Consideration will also be given to modifications of the basic OMT approach, which improve the design and implementation phases of OMT.

7. Introduction to complexity - 2 Lectures
OO notation, growth rates. Measurement of execution time of real program examples and the estimation of their time complexity. Examples of time/space trade-offs.

8. Classes of algorithm - 2 Lectures
An overview will be given on the different classes of algorithm; for example, divide and conquer and greedy algorithms. Genetic algorithms will also be discussed.

9. Storing and retrieving data by key (Internal storage) - 6 Lectures
This problem will be used to motivate the discussion of a wide variety of different implementation techniques. The features of some typical solutions will be related to the dimensions of the problem such as the volume of data to be handled, volatility and the operations required. Internal Storage: linear and binary searching. Brief introductions to proving programs correct with particular reference to the use of loop invariants. Linked representations; an introduction to hashing and binary search trees.

10. Storing and retrieving data by key (External storage) - 4 Lectures
Performance issues. Hashing and B-tree organisations. The Hashable class in Java.

11. Representing text - 4 Lectures
String matching algorithms and their performance.

12. Sorting - 4 Lectures
Divide and conquer algorithms. An introduction to proving the performance of an algorithm.