Kanban overview in Software Engineering - Time & Space Complexity

Time Complexity: Kanban overview

O(n)

Understanding Time Complexity

When using Kanban to manage work, it's important to understand how the flow of tasks affects the time it takes to complete them.

We want to see how the number of tasks impacts the time needed to finish all work.

Scenario Under Consideration

Analyze the time complexity of processing tasks in a Kanban board with a simple workflow.

// Pseudocode for processing tasks in Kanban
for each task in ToDoList:
    move task to InProgress
    work on task
    move task to Done

This code moves each task through the stages of the Kanban board one by one until all are done.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping through each task in the list.
• How many times: Once for every task in the ToDoList.

How Execution Grows With Input

As the number of tasks grows, the time to process all tasks grows in a straight line.

Input Size (n)	Approx. Operations
10	10 task moves and works
100	100 task moves and works
1000	1000 task moves and works

Pattern observation: Doubling tasks roughly doubles the work time.

Final Time Complexity

Time Complexity: O(n)

This means the time to finish all tasks grows directly with the number of tasks.

Common Mistake

[X] Wrong: "Adding more tasks won't affect how long it takes to finish all work."

[OK] Correct: Each task needs attention, so more tasks mean more total work and time.

Interview Connect

Understanding how task numbers affect completion time helps you explain workflow efficiency clearly in real projects.

Self-Check

"What if tasks could be worked on in parallel? How would the time complexity change?"