Technical debt management in Software Engineering - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is technical debt in software development?

Technical debt is the extra work that results from choosing a quick or easy solution now instead of a better approach that takes more time. It can slow down future development.

beginner

Why is managing technical debt important?

Managing technical debt helps keep software maintainable, reduces bugs, and avoids costly fixes later. It ensures the project stays healthy and development stays efficient.

beginner

Name two common causes of technical debt.

1. Rushing to meet deadlines by writing quick code.
2. Lack of clear design or documentation.

intermediate

What is a good practice to reduce technical debt?

Regularly refactoring code, writing tests, and improving documentation help reduce technical debt over time.

intermediate

How can teams track technical debt?

Teams can track technical debt by keeping a list of known issues, using code quality tools, and discussing debt during planning meetings.

What does technical debt usually cause in software projects?

A Faster bug fixes

B Increased user satisfaction

C Reduced project scope

D Slower future development

Technical debt makes future changes harder and slower because of earlier shortcuts.

Which of these is NOT a cause of technical debt?

A Writing clear documentation

B Lack of design planning

C Rushing to meet deadlines

D Skipping tests

Writing clear documentation helps prevent technical debt, not cause it.

What is a common way to manage technical debt?

A Ignoring it until problems arise

B Refactoring code regularly

C Adding more features quickly

D Avoiding code reviews

Refactoring improves code quality and reduces technical debt.

When should teams address technical debt?

A Only after a project is finished

B Only when users complain

C Continuously during development

D Never, it is unavoidable

Managing technical debt continuously keeps the project healthy.

Which tool can help identify technical debt?

A Code quality analyzer

B Graphic design software

C Email client

D Spreadsheet for budgets

Code quality analyzers detect code issues that contribute to technical debt.

Explain what technical debt is and why it matters in software projects.

Think about shortcuts in coding and their long-term effects.

Describe some ways teams can manage and reduce technical debt.

Consider regular habits that improve code quality.

Practice

1. What is technical debt in software engineering?

easy

A. Shortcuts or problems in code that need fixing later

B. Money spent on buying software tools

C. The total number of lines of code in a project

D. The time taken to write documentation

Solution

1. Step 1: Understand technical debt and compare options

   Technical debt refers to shortcuts or quick fixes in code that make future changes harder. Only "Shortcuts or problems in code that need fixing later" correctly describes technical debt as code problems needing fixing later.

2. Final Answer:

   Shortcuts or problems in code that need fixing later -> Option A

3. Quick Check:

   Technical debt = shortcuts needing future fixes [OK]

Hint: Think of debt as code shortcuts needing future repair [OK]

Common Mistakes:

• Confusing technical debt with project cost
• Thinking it means code size or documentation time
• Assuming it is about hardware or tools

2. Which of the following is a correct practice in managing technical debt?

easy

A. Regularly identifying and prioritizing debt items

B. Ignoring code problems until the project ends

C. Adding more features without fixing existing issues

D. Deleting all old code without review

Solution

1. Step 1: Identify good practices and evaluate options

   Managing technical debt means regularly finding and prioritizing problems to fix. Regularly identifying and prioritizing debt items describes this process correctly, while others ignore or worsen debt.

2. Final Answer:

   Regularly identifying and prioritizing debt items -> Option A

3. Quick Check:

   Good management = identify and prioritize [OK]

Hint: Good management means regular checks and prioritizing fixes [OK]

Common Mistakes:

• Thinking ignoring problems is okay
• Believing adding features solves debt
• Assuming deleting code blindly helps

3. Consider this code snippet representing technical debt items with priorities:

debt = {"bug1": 3, "bug2": 1, "bug3": 2}
# Lower number means higher priority
sorted_debt = sorted(debt.items(), key=lambda x: x[1])
print([item[0] for item in sorted_debt])

What will be the output?

medium

A. ['bug1', 'bug3', 'bug2']

B. ['bug3', 'bug1', 'bug2']

C. ['bug2', 'bug3', 'bug1']

D. ['bug2', 'bug1', 'bug3']

Solution

1. Step 1: Trace the sorting logic

   The dictionary has bug1=3, bug2=1, bug3=2 (lower number = higher priority). sorted() by value (x[1]) ascending orders: bug2 (1), bug3 (2), bug1 (3). List comprehension gets ['bug2', 'bug3', 'bug1'].

2. Final Answer:

   ['bug2', 'bug3', 'bug1'] -> Option C

3. Quick Check:

   Sorted by priority ascending = ['bug2', 'bug3', 'bug1'] [OK]

Hint: Sort by priority number ascending to find order [OK]

Common Mistakes:

• Sorting in descending order by mistake
• Confusing keys and values in sorting
• Misreading priority meaning

4. This code tries to remove fixed technical debt items from a list:

debt = ["bug1", "bug2", "bug3"]
fixed = ["bug2"]
for item in fixed:
    debt.remove(item)
print(debt)

What is the output?

medium

A. ['bug3']

B. ['bug1', 'bug2', 'bug3']

C. Error: list.remove() called on missing item

D. ['bug1', 'bug3']

Solution

1. Step 1: Trace the list removal

   Loop over fixed=["bug2"], remove "bug2" from debt=["bug1", "bug2", "bug3"], resulting in ["bug1", "bug3"], printed as ['bug1', 'bug3'].

2. Final Answer:

   ['bug1', 'bug3'] -> Option D

3. Quick Check:

   Removed fixed items = ['bug1', 'bug3'] [OK]

Hint: Removing fixed items updates list correctly [OK]

Common Mistakes:

• Expecting no change in list
• Thinking it causes an error
• Removing wrong items by mistake

5. A team has a list of technical debt items with severity scores:

debt = {"bug1": 5, "bug2": 0, "bug3": 3, "bug4": 0}
# Severity 0 means no real issue
# They want to create a new dict with only real issues
real_issues = {k: v for k, v in debt.items() if v != 0}
print(real_issues)

What will be printed?

hard

A. {'bug1': 5, 'bug2': 0, 'bug3': 3, 'bug4': 0}

B. {'bug1': 5, 'bug3': 3}

C. {'bug2': 0, 'bug4': 0}

D. {}

Solution

1. Step 1: Trace the dict comprehension filtering

   Keeps pairs where v != 0, excluding bug2:0 and bug4:0, resulting in {'bug1': 5, 'bug3': 3}.

2. Final Answer:

   {'bug1': 5, 'bug3': 3} -> Option B

3. Quick Check:

   Filtered dict excludes zero severity items [OK]

Hint: Filter out zero values to keep real issues [OK]

Common Mistakes:

• Including zero severity items by mistake
• Confusing keys and values in comprehension
• Expecting empty dict instead