Technical debt management in Software Engineering - Step-by-Step Execution

Concept Flow - Technical debt management

Start Project

↓

Write Code Quickly

↓

Accumulate Technical Debt

↓

Detect Debt Impact

↓

Plan Debt Repayment

↓

Refactor and Improve Code

↓

Reduce Technical Debt

↓

Continue Development

↩Back to Detect Debt Impact

This flow shows how technical debt builds up when coding fast, then is detected and managed by planning and refactoring to reduce it.

Execution Sample

Software Engineering

1. Write quick code
2. Note technical debt
3. Schedule refactoring
4. Refactor code
5. Reduce debt
6. Continue development

This sequence shows the steps of managing technical debt from creation to reduction.

Analysis Table

Step	Action	Technical Debt Level	Result
1	Write quick code without full design	Medium	Fast progress but some shortcuts
2	Technical debt accumulates	High	Code is harder to maintain
3	Detect debt impact on project	High	Recognize slowing development
4	Plan refactoring tasks	High	Schedule time to fix debt
5	Refactor code to improve quality	Low	Reduce debt and improve maintainability
6	Continue development with less debt	Low	Faster, cleaner progress
7	Repeat cycle as needed	Varies	Ongoing management of debt

💡 Process repeats to keep technical debt under control and maintain code quality

State Tracker

Variable	Start	After Step 2	After Step 5	Final
Technical Debt Level	None	High	Low	Low

Key Insights - 3 Insights

Why does technical debt increase when writing code quickly?

Because shortcuts and incomplete design decisions are made to save time, as shown in step 1 and 2 of the execution_table.

How does refactoring help manage technical debt?

Refactoring improves code quality and reduces debt by fixing shortcuts, as seen in step 5 where debt level drops from High to Low.

Why must technical debt management be an ongoing process?

Because new debt can accumulate as development continues, so the cycle repeats to keep debt controlled, shown in step 7.

Visual Quiz - 3 Questions

Look at the execution_table, what is the technical debt level after writing quick code?

A Medium

B Low

C High

D None

💡 Hint

Check Step 1 and Step 2 in the execution_table for debt levels after writing code quickly.

At which step does the technical debt level reduce significantly?

A Step 3

B Step 5

C Step 2

D Step 6

💡 Hint

Look at the technical debt level column in the execution_table to find where it drops.

If refactoring is skipped, how would the technical debt level change over time?

A It would stay low

B It would immediately drop

C It would increase

D It would disappear

💡 Hint

Refer to the variable_tracker and execution_table showing debt accumulation without reduction.

Concept Snapshot

Technical debt is the extra work caused by quick, imperfect coding.
It builds up over time and slows development.
Managing it means detecting debt, planning fixes, and refactoring code.
This cycle repeats to keep code clean and maintainable.
Ignoring debt leads to harder, slower development.

Full Transcript

Technical debt management involves recognizing that writing code quickly can cause shortcuts and incomplete design, which accumulate as technical debt. This debt makes the code harder to maintain and slows down future development. The process starts with writing quick code, which increases debt. Then, the team detects the impact of this debt and plans refactoring tasks to improve the code quality. Refactoring reduces the technical debt level, making the code easier to work with. Development continues with less debt, but since new debt can form, this cycle repeats. Managing technical debt is essential to keep software projects healthy and maintainable over time.

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