How to write pseudocode: A guided tutorial

Core pseudocode constructs

Programming logic flows through sequences, decisions and loops. The following pseudocode constructs, each supplemented by a general workflow example, form the basis of algorithmic problem-solving and enable developers to model real-world processes -- from edge case troubleshooting in algorithm behavior to creating phased implementation roadmaps that balance technical debt with feature priorities.

Sequential operations

A program starts with steps that execute one after another. While this might seem obvious, mastering sequential flow, or SEQUENCE, in pseudocode is essential for addressing more complex logic. Consider the following user registration workflow and notice how sequential operations dictate the mandatory steps:

INPUT user_data

VALIDATE user_data

PROCESS user_data

OUTPUT results

Decision making

Program flow rarely follows a linear path. It requires branching logic through IF-THEN-ELSE constructs to create the dynamic behavior, like the following age-gated content access:

IF age < 18 THEN

    DISPLAY "Access denied"

ELSE

    GRANT access

ENDIF

Different conditional scenarios call for different approaches. Use CASE statements to clarify logic and intent when multiple conditions are involved. For example, the following workflow can process different payment methods depending on the input:

CASE payment_method OF

    "credit": PROCESS credit_payment

    "debit": PROCESS debit_payment

    "crypto": PROCESS crypto_payment

ENDCASE

CASE statements are optimized for evaluating multiple values of a single variable, whereas IF statements are more suitable for Boolean conditions and complex logical expressions involving multiple variables. Additionally, compiler optimizations can execute CASE statements faster through jump tables.

Loops and iterations

Loops automate repetitive tasks by executing code blocks multiple times based on specified conditions. They're essential for processing data collections, running calculations iteratively or maintaining continuous operations. FOR loops are the best choice when the exact number of iterations is known, as demonstrated in the following shopping cart calculation:

FOR each item IN shopping_cart

    CALCULATE item_total

    ADD TO cart_total

ENDFOR

But what about dynamic conditions? WHILE loops can effectively handle these scenarios, as shown in the following network connection example:

WHILE connection_active

    PROCESS incoming_data

    CHECK connection_status

ENDWHILE

REPEAT-UNTIL loops offer similar functionality to WHILE but guarantee at least one execution since they check the condition after running the code block, as illustrated in the following input validation example:

REPEAT

    GET user_input

    VALIDATE input

UNTIL input_valid

Function handling

Functions break down complex operations into smaller, digestible chunks. They accept parameters, process data and return results while maintaining scope isolation. Consider the following pseudocode for validating user credentials:

FUNCTION validate_user(username, password)

    CHECK username_exists

    VERIFY password_hash

    RETURN validation_status

ENDFUNCTION

Exception management

Runtime errors are inevitable when processing user input, accessing external resources or performing complex calculations. TRY-CATCH blocks can help plan for potential errors, as shown in the following example that flags an anomaly when processing sensitive data and notifies the admin of it:

TRY

    PROCESS sensitive_data

CATCH data_error

    LOG error_details

    NOTIFY admin

ENDTRY

Key guidelines for writing pseudocode

Effective pseudocode balances expressiveness with readability, clearly communicating intent while remaining implementation-agnostic. Here are some tips and general best practices for optimizing the pseudocode process:

Syntax guidelines

• Write keywords in UPPERCASE. Keywords serve as signposts of the code's logic. WHILE, IF, FUNCTION -- these stand out visually and help clarify flow.
• Place each statement on its own line.

Naming conventions

• Skip cryptic variable names and use_descriptive_names that tell the data's story. Whether it's customer_record or temp_buffer, make what the code entails obvious.
• Stick to action verbs for functions, such as calculate_total(), validate_input() or process_transaction().

Structure and flow

• Keep statements concise but complete. Pseudocode should read like a well-written technical document. Instead of something like "x = x + 1", write "INCREMENT counter BY 1" to maintain language independence.
• Use proper indentation to indicate the logical hierarchy of the algorithm and nest constructs thoughtfully. Each level of indentation signals a new logical scope, as illustrated here:

IF user_authenticated THEN

    FOR each_permission IN user_permissions

        IF permission_active THEN

            GRANT access_level

        ENDIF

    ENDFOR

ENDIF

• Explicitly close constructs with ENDIF, ENDWHILE or ENDFOR. These markers prevent logical leaks and make the pseudocode self-documenting.

How to write pseudocode: Practical applications

Pseudocode might be overkill for simple CRUD operations or straightforward implementations, but it is a valuable communication tool for algorithmic challenges, system design or even team discussions. The following examples demonstrate building an API rate limiter and writing a bubble sort algorithm using pseudocode.

Build an API rate limiter with pseudocode

Rate limiting might seem simple on the surface -- just count requests and block incoming requests when they exceed a certain threshold. But dig deeper, and it's possible to find a maze of edge cases that can bring down an API if not properly handled.

Let's create a rate limiter using pseudocode that caps users at 100 requests per hour. This implementation will also consider distributed systems, race conditions and temporary traffic spikes.

First, the core rate-checking logic:

FUNCTION check_rate_limit(user_id)

    DEFINE requests_limit = 100

    DEFINE time_window = 3600  // seconds in an hour

    GET current_timestamp

    REMOVE expired_requests(user_id, current_timestamp - time_window)

    GET request_count FOR user_id IN time_window

    IF request_count >= requests_limit THEN

        RETURN rate_limit_exceeded

    ENDIF

    ADD new_request(user_id, current_timestamp)

    RETURN request_accepted

ENDFUNCTION

In a distributed system, multiple servers might try updating the same user's request count simultaneously. Atomic operations can prevent race conditions.

FUNCTION add_new_request(user_id, timestamp)

    TRY

        LOCK user_requests_table

        INCREMENT request_counter

        ADD request_record(user_id, timestamp)

        RELEASE lock

    CATCH concurrency_error

        HANDLE distributed_system_error

        RETURN system_busy

    ENDTRY

ENDFUNCTION

But how to manage traffic spikes? Simple counters won't cut it when an API faces sudden request surges. For finer control over API traffic, use a layered approach that combines fixed rate limits with burst handling. The burst handler acts as a pressure valve, preventing legitimate traffic spikes from being incorrectly throttled while protecting the system from abuse.

FUNCTION handle_burst_traffic(user_id, timestamp)

    GET current_window_stats

    IF request_spike_detected THEN

        CALCULATE burst_allowance

        IF within_burst_limits THEN

            INCREMENT burst_counter

            RETURN request_accepted

        ENDIF

    ENDIF

    RETURN regular_rate_check(user_id)

ENDFUNCTION

Bubble sort with pseudocode

A bubble sort algorithm is a way to order numbers in a list by comparing them. Here's how a developer might write a sorting algorithm if they're in a rush:

function sort(arr)

    n = length(arr)

    for i = 0 to n

        for j = 0 to n

            if arr[j] > arr[j+1]

                t = arr[j]

                arr[j] = arr[j+1]

                arr[j+1] = t

    return arr

Notice any issues? There are missing bounds checks, unclear variable names, no error handling and incorrect loop termination conditions. This implementation could lead to array index errors.

Here's a more structured implementation of the same bubble sort:

FUNCTION bubble_sort(number_array)

    DEFINE array_size = LENGTH OF number_array

    IF array_size <= 1 THEN

        RETURN number_array

    ENDIF

    FOR outer_index = 0 TO array_size - 2

        FOR inner_index = 0 TO array_size - outer_index - 2

            IF number_array[inner_index] >

number_array[inner_index + 1] THEN

                SWAP number_array[inner_index] WITH

number_array[inner_index + 1]

            ENDIF

        ENDFOR

    ENDFOR

    RETURN number_array

ENDFUNCTION

Review the following key improvements:

• Descriptive variable names -- number_array vs. arr.
• Proper bounds checking to prevent array overruns.
• Clear nested loop structure with correct termination conditions.
• Explicit SWAP operation instead of cryptic assignments.
• Input validation for array size.
• Consistent indentation demonstrating a logical flow.

The second version of the bubble sort algorithm communicates logic and protects against edge cases. A developer could use this version to follow the sorting process -- from the outer loop controlling passes to the inner loop handling element comparisons.

Keep in mind, however, that pseudocode can't catch implementation-specific issues like memory management or type constraints. In the bubble sort example, the pseudocode shows the sorting logic but doesn't address performance optimizations or space complexity concerns that become critical in real-world applications.

Twain Taylor is a technical writer, musician and runner. Having started his career at Google in its early days, today, Twain is an accomplished tech influencer. He works closely with startups and journals in the cloud-native space.