Inputs & Outputs

Thinking Ahead

Problem-solving in computer science involves planning for inputs, outputs, and conditions, using tools like caching and reusable components.

Identifying inputs and outputs

When solving a problem in computer science, it is important to first identify what goes into the system and what comes out.
Inputs are the data that the program receives.
Outputs are the results that the program produces.
- In a simple calculator program, the inputs would be numbers and operations like addition or subtraction.
- The outputs would be the results of these calculations.

Determining preconditions

Preconditions are the conditions that need to be true before a program or algorithm can run correctly.
These are important because they help ensure that the program works as expected.
- In a program that divides two numbers, a precondition might be that the second number (the divisor) cannot be zero.
Without this check, the program could crash or give incorrect results.

Caching

Caching is a technique used to speed up computer processes by storing frequently accessed data in a faster memory location.
- This can reduce the time it takes to retrieve data from a slower storage medium, such as a hard drive.
A good example of caching is web browsers storing parts of a webpage so that when the user revisits the page, it loads faster.
The drawback of caching is that the stored data can become outdated, leading to incorrect results if the data is not refreshed.

Reusable program components

Reusable program components are parts of a program that can be used in multiple places without rewriting the code.
These components make coding more efficient and reduce errors because programmers can rely on tried and tested code.
A function that calculates the area of a rectangle could be reused whenever this calculation is needed.
- This saves time and ensures consistency.

1Components of a Computer

2Software & Software Development

2.1Systems Software

2.1.1Operating Systems

2.1.2Managing Memory

2.1.3Paging

2.1.4Segmentation

2.1.5Device Management & Drivers

2.1.6Interrupts

2.1.7Interrupt Service Routine (ISR)

2.1.8Scheduling

2.1.9Types of Scheduling

2.1.10Types of Operating System

2.1.11BIOS

2.1.12Virtual Machines

2.2Applications Generation

2.2.1Applications Software

2.2.2Utilities

2.2.3Encryption Software

2.2.4Defragmentation Software

2.2.5Data Compression Software

2.2.6Backup Software

2.2.7Open Source Software

2.2.8Proprietary Software

2.2.9Licensing Issues

2.2.10Compilers

2.2.11Interpreters

2.2.12Assemblers

2.2.13Compiling a Program

2.2.14Lexical Analysis

2.2.15Compilation Stages

2.2.16Linkers, Loaders & Libraries

2.3Software Development

2.3.1Algorithmic Thinking

2.3.2Waterfall Lifecycle

2.3.3Waterfall Lifecycle - Strengths & Weaknesses

2.3.4Agile Methodology

2.3.5Agile Methodology - Strengths & Weaknesses

2.3.6Extreme Programming

2.3.7Extreme Programming - Strengths & Weaknesses

2.3.8Spiral Methodology

2.3.9Spiral Methodology - Strengths & Weaknesses

2.3.10Rapid Application Development

2.3.11RAD - Strengths & Weaknesse

2.4Types of Programming Language

2.4.1High Level Programming Languages

2.4.2Low Level Programming Languages

2.4.3Programming Paradigms

2.4.4Procedural Programming Languages

2.4.5Assembly

2.4.6Machine Code

2.4.7Modes of Addressing Memory

2.4.8Object-Oriented Languages

2.4.9Classes

2.4.10Objects

2.4.11Encapsulation

2.4.12Polymorphism

3Exchanging Data

3.1Compression, Encryption & Hashing

3.1.1Compression

3.1.2Run Length Encoding

3.1.3Dictionary Coding

3.1.4Encryption

3.1.5Symmetric Encyrption

3.1.6Hashing

3.2Databases

3.2.1Records

3.2.2Databases

3.2.3Relational Databases

3.2.4Entity Relationship Modelling

3.2.5Managing Databases

3.2.6Normalisation

3.2.7SQL

3.2.8Principles of Data Storage

3.2.9Atomicity, Consistency, Isolation & Durability

3.3Networks

3.4Web Technologies

3.4.1HTML

3.4.2CSS

3.4.3JavaScript

3.4.4Search Engine Indexing

3.4.5PageRank Algorithm

3.4.6Server & Client-Side Processing

4Data Types, Data Structures & Algorithms

4.1Data Types

4.2Data Structures

4.2.13D Arrays

4.2.2Linked List

4.2.3Queues

4.2.4Graphs

4.2.5Hash Tables

4.2.6Stacks

4.2.7Trees

4.2.8Binary Search Trees

4.3Boolean Algebra

4.3.1Binary Systems

4.3.2Logic Circuits

4.3.3Boolean Operators

4.3.4Evaluating Expressions

4.3.5Boolean Logic

4.3.6Karnaugh Maps

4.3.7Boolean Identities

4.3.8Boolean Rules

4.3.9Flip Flops

4.3.10Adder Circuits

5Legal, Moral, Cultural & Ethical Issues

5.1Computing Related Legislation

5.1.1Data Protection Act (1998)

5.1.2Computer Misuse Act (1990)

5.1.3Copyright Designs & Patents Act (1988)

5.1.4Creative Commons Licensing

5.1.5Regulation of Investigatory Powers Act (2000)

5.2Moral & Ethical Issues

5.2.1Online Activity Tracking

5.2.2Censorship

5.2.3Positive Cultural Impacts

5.2.4Negative Cultural Impacts

5.2.5E-Waste

5.2.6Energy Consumption

5.2.7Positive Environmental Impact

5.2.8Layout, Colour Paradigms & Character Sets

5.2.9Computers in the Workplace

5.2.10Automated Decision-Making

5.2.11Artificial Intelligence

5.2.12Monitoring Behaviour

5.2.13Analysing Personal Information

5.2.14Piracy & Offensive Communication

6Elements of Computational Thinking

6.1Thinking Abstractly

6.1.1Abstraction

6.2Thinking Ahead

6.2.1Inputs & Outputs

6.3Thinking Procedurally

6.3.1Decomposition

6.4Thinking Logically

6.4.1Pseudocode

6.4.2Flow Diagrams

6.5Thinking Concurrently

6.5.1Concurrent Processing

7Problem Solving & Programming

7.1Programming Techniques

7.1.1Variables & Constants

7.1.2Comments

7.1.3Inputs & Outputs

7.1.4Subroutines

7.1.5Scope

7.2Programming Construction

7.2.1Integrated Development Environments

7.2.2The Purpose of Testing

7.2.3Types of Testing

7.2.4Test Plans

8Algorithms

8.1Algorithms

8.1.1Interpreting Algorithms

8.1.2Correcting Algorithms

8.1.3Completing Algorithms

8.1.4Sort Algorithms

8.1.5Bubble Sort

8.1.6Insertion Sort

8.1.7Merge Sort

8.1.8Search Algorithms

8.1.9Linear Search

8.1.10Binary Search

8.1.11Quick Sort Algorithm

8.1.12A* Algorithm

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