Lecture Notes: IGCSE Computer Science - Chapter 7: Algorithm Design and Problem Solving

Overview

• Focus: Chapter 7 of the IGCSE Computer Science syllabus, covering algorithm design and problem solving.
• Purpose: Establish a solid foundation for programming.

Key Topics

1. Program Development Life Cycle (PDLC)

   • A process followed by developers when creating a program
   • Stages:
     1. Analysis
     2. Design
     3. Coding
     4. Testing
   • Example: App development process (e.g., PowerPoint, Zoom, Facebook)

2. Analysis Phase

   • Requirement Specification: Define what the program aims to achieve.
   • Steps:
     • Decomposition: Break down complex problems into smaller, manageable parts.
     • Abstraction: Keep only necessary elements for solution.

3. Design Phase

   • Continuation from decomposition.
   • Methods:
     1. Structure Diagram
     2. Flowchart
     3. Pseudocode

4. Coding Phase

   • Translating design into a functional program using pseudocode and flowcharts.

5. Testing Phase

   • Types of Testing:
     • Unit Testing: Test individual parts of the code.
     • Integration Testing: Test the program as a whole.
   • Program Development Life Cycle (PDLC)
     • It's a cycle: After testing, developers return to analysis to add new features.

Computer Systems and Subsystems

• Components: Software, data, hardware, communication, and people.
• Example: Instagram app divided into subsystems (e.g., story function, post function).
• Design Questions:
  • How to divide systems into subsystems.
  • How to develop subsystems using appropriate programming languages.

Designing Programs

1. Structure Diagram

   • Top-down design, breaking down big problems into smaller parts.
   • Example: Alarm clock specification.

2. Flowchart

   • Visual tool to model algorithms.
   • Key components: Terminal, Process, Input/Output, Subroutine, Flow Line.
   • Example: Designing algorithms using flowcharts.

3. Pseudocode

   • Simplified code to represent algorithms.
   • Key elements: Assignment, Input/Output, Conditionals, Iterative Statements.

Standard Methods of Solutions

• Operations: Totaling, counting, finding max/min, averaging, linear search, bubble sort.
• Validation and Verification:
  • Validation: Ensure data input is reasonable (range check, length check, etc.).
  • Verification: Double-entry and visual checks to ensure correct data entry.

Testing Strategies

• Types of Data: Normal, abnormal, extreme, boundary.
• Trace Tables: Record variable values at each step to test algorithms.
• Identifying Errors: Use trace tables and self-generated test data to verify algorithm accuracy.

Conclusion

• Importance of Practice: Emphasized throughout the lecture as crucial for mastering programming concepts.
• Next Steps: Dive deeper into programming with a focus on Python in future chapters.