Regression testing in Software Engineering - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Purpose of Regression Testing in ML Models

Why is regression testing important when updating a machine learning model?

A To ensure new changes do not break existing model predictions

B To increase the training speed of the model

C To reduce the size of the training dataset

D To improve the model's ability to learn new features

💡 Hint

Think about what could happen if changes cause unexpected errors.

Regression testing checks that updates or fixes do not cause old features or predictions to fail.

❓ Metrics

intermediate

Detecting Regression with Metrics

Which metric change would best indicate a regression in a regression model's performance after an update?

A Increase in Mean Squared Error (MSE)

B Increase in training speed

C Decrease in model size

D Increase in number of features

💡 Hint

Think about what a higher error means for model predictions.

An increase in MSE means the model's predictions are less accurate, indicating regression.

🔍 Analysis

advanced

Identifying Regression Cause in Code

After retraining a model, predictions are worse. Which code change is most likely causing regression?

Software Engineering

old code:
model.fit(X_train, y_train)
new code:
model.fit(X_train, y_train, epochs=1)

A Increasing epochs causes overfitting

B Changing the dataset causes regression

C Reducing training epochs to 1 causes underfitting

D Using a different optimizer causes regression

💡 Hint

Think about how training time affects learning.

Training for only 1 epoch may not be enough for the model to learn patterns, causing worse predictions.

❓ Model Choice

advanced

Choosing a Model for Regression Testing

Which model type is best suited for regression testing to detect performance drops on continuous output tasks?

A K-Means Clustering

B Linear Regression

C Decision Tree Classifier

D Naive Bayes Classifier

💡 Hint

Consider the type of output the model predicts.

Linear Regression predicts continuous values, making it suitable for regression testing on continuous outputs.

🔍 Analysis

expert

Output of Regression Test Code Snippet

What is the output of this Python code simulating a regression test on model predictions?

Software Engineering

old_preds = [2.5, 0.0, 2.1, 7.8]
new_preds = [2.7, 0.1, 2.0, 7.9]
threshold = 0.15
regression_detected = any(abs(o - n) > threshold for o, n in zip(old_preds, new_preds))
print(regression_detected)

A TypeError

B False

C SyntaxError

D True

💡 Hint

Check if any prediction difference exceeds the threshold.

The difference between 2.5 and 2.7 is 0.2, which is greater than 0.15, so regression_detected is True.