ap csp final

debug

Finding and fixing errors in code.

syntax error

Mistakes in the structure of the code (like missing a bracket).

logical error

Code runs but gives wrong results due to flawed logic.

runtime error

Error that happens while the program is running.

console.log & watchers

Tools to help monitor what’s happening in code.

3 parts of a loop

Initialization, Condition, Update.

WHILE loop

Runs while a condition is true.

FOR loop

Runs a fixed number of times.

FOR EACH loop

Loops through each item in a list.

data structure

A way to organize and store data.

list

A collection of items in a specific order.

array

Another name for a list; often used in JavaScript.

dictionary

A data structure with key-value pairs.

element

An item in a list or array.

index

The position of an element in a list (starts at 0).

appendItem

Adds an item to the end of a list.

insertItem

Adds an item at a specific position in a list.

removeItem

Deletes an item from a list.

list.length

Returns the number of items in a list.

data abstraction

Hiding details to make complex data easier to use.

substring

A part of a string.

traversal

Going through each item in a list or array.

filter

Creates a new list with items that match a condition.

reduce

Combines a list into a single value (like sum or product).

parameter

A variable in a function definition.

argument

The actual value passed into a function.

return

Sends a result back from a function.

scope (local vs. global)

Where a variable can be used (inside or outside a function).

procedural abstraction

Using functions to simplify complex code.

library

A collection of prewritten code.

API

A set of tools and functions for building software (e.g., Google Maps API).

dot syntax

Accessing object properties or methods (object.property).

algorithm

A step-by-step process to solve a problem.

sequencing

The order of steps in a program.

selection

Using conditions (if/else) to make decisions.

iteration

Repeating actions using loops.

algorithmic efficiency

How well an algorithm uses time or resources.

linear search

Checking each item in a list one by one.

binary search

Quick search that splits sorted data in half each time.

big O notation

Describes how fast an algorithm grows (e.g., O(n), O(log n)).

logarithmic

Growth slows down as data size increases.

polynomial

Reasonable time growth (like n^2).

exponential

Extremely fast growth (like 2^n).

factorial

Fast growth (like n!); grows faster than exponential.

unreasonable vs. reasonable time

Unreasonable time means it takes too long to compute for big inputs.

decision problem

A yes/no problem.

optimization problem

Finding the best solution from many options.

undecidable problem

No algorithm can solve all cases of this problem.

heuristic

A shortcut or rule-of-thumb to find a good-enough solution.

parallel computing

Multiple tasks running at the same time on different processors.

distributed computing

Tasks spread across multiple connected computers.