Understand the program development life cycle
📚 Algorithm Design & Problem‑Solving: The Program Development Life Cycle
What is the Program Development Life Cycle (PDLC)?
Think of it as the recipe for cooking a dish. You plan, write, test, and refine your code just like you’d pre‑heat the oven, mix the ingredients, taste the sauce, and adjust the seasoning until it’s perfect.
In the Cambridge IGCSE Computer Science 0478 syllabus, the PDLC is a series of stages that guide you from an idea to a working program.
🛠️ Stages of the PDLC
| Stage | What Happens? | Example |
|---|---|---|
| Requirements | Define what the program must do. | “Create a calculator that adds two numbers.” |
| Design | Plan the structure and algorithm. | Sketch a flowchart or pseudocode. |
| Implementation | Write the actual code. | Python code for the calculator. |
| Testing | Run the program and check for errors. | Enter 3 + 5 and verify 8 is returned. |
| Maintenance | Fix bugs or add features later. | Add a subtraction function. |
🧩 Algorithm Design Techniques
Divide & Conquer
Split a problem into smaller, similar sub‑problems, solve each, then combine the results.
Example: Merge Sort splits an array, sorts each half, then merges them.
Greedy
Make the locally best choice at each step, hoping it leads to a global optimum.
Example: Coin Change – always pick the largest coin that doesn’t exceed the remaining amount.
Brute Force
Try every possible solution until you find the correct one.
Example: Finding the maximum in a list by checking each element.
Dynamic Programming
Store results of sub‑problems to avoid recomputation.
Example: Fibonacci numbers using memoisation.
🧠 Problem‑Solving Strategies
- Understand the problem – read carefully, identify inputs/outputs.
- Plan – write pseudocode or draw a flowchart.
- Implement – translate the plan into code.
- Test – use test cases, including edge cases.
- Debug – trace errors, use print statements or a debugger.
- Optimize – improve time/space complexity if needed.
Remember the Big‑O notation to describe algorithm efficiency: $O(n)$ is linear, $O(n^2)$ is quadratic, $O(\log n)$ is logarithmic.
✏️ Examination Tips
Read the question carefully. Highlight keywords like “calculate”, “sort”, “find the maximum”.
Show your work. Write clear pseudocode or flowcharts before coding.
Check edge cases. Test with minimum and maximum values.
Use comments. Explain what each part of your code does.
Time management. Allocate time for planning and testing – don’t skip the design stage!
💡 Remember: A well‑planned algorithm is like a well‑built LEGO set – it’s easier to assemble, less likely to break, and you’ll enjoy the final build more.
Revision
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