Risk-based testing in Testing Fundamentals - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Risk Prioritization in Testing

In risk-based testing, which factor is most important when deciding which features to test first?

A The likelihood of failure and impact on users

B The complexity of the code implementation

C The number of developers who worked on the feature

D The order in which features were developed

💡 Hint

Think about what risk means in testing: what could go wrong and how bad it would be.

Risk-based testing focuses on testing features that are most likely to fail and would cause the biggest problems if they do. This helps use testing time wisely.

❓ Predict Output

intermediate

Output of Risk Score Calculation

What is the output of this Python code that calculates risk scores for features?

Testing Fundamentals

features = {'Login': {'likelihood': 0.9, 'impact': 0.8}, 'Search': {'likelihood': 0.4, 'impact': 0.5}}
risk_scores = {f: data['likelihood'] * data['impact'] for f, data in features.items()}
print(risk_scores)

A {'Login': 1.7, 'Search': 0.9}

B {'Login': 0.72, 'Search': 0.2}

C {'Login': 0.9, 'Search': 0.4}

D {'Login': 0.8, 'Search': 0.5}

💡 Hint

Multiply likelihood by impact for each feature.

Risk score = likelihood * impact. For Login: 0.9 * 0.8 = 0.72. For Search: 0.4 * 0.5 = 0.2.

❓ assertion

advanced

Validating Risk Priority Order

Given a list of features with risk scores, which assertion correctly checks that the features are sorted from highest to lowest risk?

Testing Fundamentals

features = [('Login', 0.72), ('Search', 0.2), ('Profile', 0.5)]
sorted_features = sorted(features, key=lambda x: x[1], reverse=True)

A assert sorted_features[0][1] < sorted_features[-1][1]

B assert sorted_features == [('Search', 0.2), ('Profile', 0.5), ('Login', 0.72)]

C assert sorted_features[0][0] == 'Search'

D assert sorted_features == [('Login', 0.72), ('Profile', 0.5), ('Search', 0.2)]

💡 Hint

Check that the list is sorted descending by risk score.

The sorted list should start with the highest risk score (0.72 for Login), then 0.5 for Profile, then 0.2 for Search.

🔧 Debug

advanced

Debugging Risk Score Calculation Error

Why does this code raise a TypeError when calculating risk scores?

Testing Fundamentals

features = {'Login': {'likelihood': '0.9', 'impact': 0.8}}
risk_scores = {f: data['likelihood'] * data['impact'] for f, data in features.items()}
print(risk_scores)

A Because the print statement is missing parentheses

B Because the dictionary keys are strings instead of integers

C Because 'likelihood' is a string and cannot be multiplied by a float

D Because the dictionary comprehension syntax is incorrect

💡 Hint

Check the data types of values used in multiplication.

Multiplying a string ('0.9') by a float (0.8) causes a TypeError in Python.

❓ framework

expert

Designing a Risk-Based Test Suite

You have 5 features with risk scores: A(0.9), B(0.7), C(0.4), D(0.3), E(0.1). You can only write tests for 3 features due to time limits. Which selection best follows risk-based testing principles?

A Test features A, B, and C because they have the highest risk scores

B Test features C, D, and E because they are less complex

C Test features B, D, and E because they were developed last

D Test features A, C, and E to cover a range of risk scores

💡 Hint

Focus on features with the highest risk scores first.

Risk-based testing prioritizes testing features with the highest risk to catch critical issues early.