Risk analysis (probability and impact) in Software Engineering - Mini Project: Build & Apply

Risk Analysis: Probability and Impact

📖 Scenario: You are part of a project team planning a new software product. To manage risks effectively, you need to list potential risks, assign their probability and impact scores, and then identify which risks need urgent attention.

🎯 Goal: Build a simple risk analysis table that shows each risk with its probability and impact scores, and then filter out the high-risk items that need immediate focus.

📋 What You'll Learn

Create a dictionary called risks with exactly these entries: 'Data loss': 0.3, 'Security breach': 0.6, 'Performance lag': 0.4, 'User error': 0.2, 'Hardware failure': 0.5

Create a dictionary called impact with exactly these entries: 'Data loss': 9, 'Security breach': 10, 'Performance lag': 6, 'User error': 3, 'Hardware failure': 7

Create a variable called threshold and set it to 4.0

Create a dictionary called high_risks that includes only risks where the product of probability and impact is greater than threshold

💡 Why This Matters

🌍 Real World

Risk analysis helps project teams focus on the most critical risks that could affect project success.

💼 Career

Understanding how to quantify and filter risks is essential for project managers, software engineers, and quality assurance professionals.

1

Set up the risk probabilities dictionary

Create a dictionary called risks with these exact entries: 'Data loss': 0.3, 'Security breach': 0.6, 'Performance lag': 0.4, 'User error': 0.2, 'Hardware failure': 0.5

Software Engineering

# Create the risks dictionary with given probabilities
# Your code here

Need a hint?

Use curly braces {} to create the dictionary with the exact keys and values.

2

Set up the impact scores dictionary

Create a dictionary called impact with these exact entries: 'Data loss': 9, 'Security breach': 10, 'Performance lag': 6, 'User error': 3, 'Hardware failure': 7

Software Engineering

risks = {
    'Data loss': 0.3,
    'Security breach': 0.6,
    'Performance lag': 0.4,
    'User error': 0.2,
    'Hardware failure': 0.5
}
# Create the impact dictionary with given scores
# Your code here

Need a hint?

Use a dictionary to map each risk to its impact score exactly as given.

3

Set the risk threshold value

Create a variable called threshold and set it to the float value 4.0

Software Engineering

risks = {
    'Data loss': 0.3,
    'Security breach': 0.6,
    'Performance lag': 0.4,
    'User error': 0.2,
    'Hardware failure': 0.5
}
impact = {
    'Data loss': 9,
    'Security breach': 10,
    'Performance lag': 6,
    'User error': 3,
    'Hardware failure': 7
}
# Set the threshold variable to 4.0
# Your code here

Need a hint?

Just assign the number 4.0 to the variable named threshold.

4

Identify high-risk items

Create a dictionary called high_risks that includes only the risks where the product of probability from risks and impact from impact is greater than threshold. Use a dictionary comprehension with variables risk and prob iterating over risks.items().

Software Engineering

risks = {
    'Data loss': 0.3,
    'Security breach': 0.6,
    'Performance lag': 0.4,
    'User error': 0.2,
    'Hardware failure': 0.5
}
impact = {
    'Data loss': 9,
    'Security breach': 10,
    'Performance lag': 6,
    'User error': 3,
    'Hardware failure': 7
}
threshold = 4.0
# Create high_risks dictionary with risks having probability * impact > threshold
# Your code here

Need a hint?

Use a dictionary comprehension to filter risks where probability times impact is greater than threshold.