Modular Refactoring in PL/SQL: Clean Up Legacy Code

Legacy PL/SQL codebases often evolve into sprawling monoliths — long procedures packed with intertwined logic, making maintenance a nightmare. This chaos slows down development, increases the risk of bugs, and frustrates developers. But there’s a way out: modular refactoring — breaking large procedures into smaller, reusable, and testable components.

In this article, I’ll share practical strategies to tame chaotic PL/SQL code by applying modular design principles. You’ll learn why modularity matters, how to identify refactoring opportunities, and what patterns help to create clean, maintainable PL/SQL packages.

Throughout my career, I’ve often encountered code that was so complex and tangled that understanding it required hours of digging through its logic. I’d find myself wondering how such code was even written in the first place. Sometimes, the answer is simple: there was no senior developer around to guide the less experienced ones. In those cases, it’s clearly a matter of limited knowledge or experience.

But that’s not always the cause. In many situations, messy code is the result of missing initial design. Code is written quickly to meet immediate needs, with no long-term plan. Over time, as requirements grow, new logic and features are piled onto existing procedures, instead of being structured into new, dedicated components. It’s also common to see procedures handling too many responsibilities, trying to do everything at once.

Another major factor is a lack of awareness about the importance of modularity — and developers unintentionally end up creating monolithic code. And finally, let’s not forget the most classic one: tight deadlines. Under pressure, teams resort to quick fixes and shortcuts rather than proper refactoring.

All of this negatively impacts maintenance and development speed in many ways. First of all, large and complex procedures are difficult to understand, making it harder for developers to locate bugs or implement enhancements. With every change introduced into such code, the risk of errors increases. Simply deciphering the existing logic slows down every implementation or modification.

Testing this kind of code can be a real nightmare. It’s much harder to isolate specific pieces of logic, which makes the code less reliable over time. What’s more, monolithic code is rarely reusable — developers end up rewriting similar logic in multiple places, which is deeply inefficient. I’ve seen separate procedures calculating the exact same thing, just for different time periods. And of course, if the calculation method changed in one, you had to remember to update the others. It’s an easy thing to overlook — and a common source of bugs.

Ultimately, the time and effort required to maintain and update monolithic procedures leads to significantly higher long-term maintenance costs.

What Is Modular Refactoring?

Modular refactoring involves restructuring existing code into smaller, independent, and reusable components. This process improves readability, maintainability, and testability by organizing the code into clear, logical units.

Key principles of modularity:

Single Responsibility Principle
Each module or component should handle one specific responsibility. This reduces complexity and improves clarity.
Encapsulation
Modules should hide their internal details and expose only what is necessary. This promotes separation of concerns - meaning each part of the code is responsible for a single, well-defined task - and reduces unwanted dependencies.
Reusability
Components should be designed in a way that allows them to be reused across different parts of the application, minimizing redundancy and development effort.
Loose Coupling
Modules should have minimal dependencies on one another, so they can be modified independently without introducing side effects elsewhere.
High Cohesion
Related functionalities should be grouped within the same module. A cohesive module stays focused and efficient.

By breaking down large procedures into smaller, well-defined modules, the code becomes significantly easier to read and understand. Each module should have a clear and specific purpose — something that should be reflected both in the naming and through concise, meaningful comments.

Such modules are also easier to test. Isolated components allow to validate logic and catch bugs early without the noise of unrelated code. This structure builds a stronger foundation for both development and maintenance.

By following these principles, modular refactoring helps transform messy, monolithic procedures into a clean, manageable, and scalable PL/SQL codebase.

Step-by-Step Modular Refactoring Process

Process of refactoring can be tricky, so the first and most important step is a careful analysis. Always begin by thoroughly examining the existing procedures to understand their flow, structure and functionality. Identify areas where the code is overly complex, such as multiple nested CASE statements, or where the ELSE branch act as a catch-all “junk-box” for undefined cases. Also look for procedures handling multiple responsibilities within single block.

Once you have mapped out the logic, start identifying logical units - distinct functionalities or responsibilities that can be separated into individual components. This step involves recognizing patterns and commonalities within the code that can be modularized.

The next important step is creating helper procedures and functions. What does it mean? From the previous step, you already know logical units. Now you can encapsulate them into smaller components - procedures or functions - each focused on specific tasks or calculations. This make code easier to manage, test and modify later when you need to add new logic or perform further refactoring.

Oracle offers us very convenient tool - packages. Use them to group related logic together. This keeps your codebase clean, organized, easier to maintain and reuse. Packages also help encapsulate and manage dependencies effectively, improving overall code quality.

To better illustrate the process, let’s take a look at two samples below. The first one is a monolithic procedure that calculates bonus and tax for employees. We can identify distinct logical units within it and then separate those responsibilities into smaller, focused components in the refactored version.

CREATE OR REPLACE PROCEDURE process_employee_data IS
  v_bonus NUMBER;
  v_tax NUMBER;
BEGIN
  FOR rec IN (SELECT employee_id, salary, department_id FROM employees) LOOP
    -- calculate bonus
    IF rec.department_id = 10 THEN
      v_bonus := rec.salary * 0.1;
    ELSIF rec.department_id = 20 THEN
      v_bonus := rec.salary * 0.15;
    ELSE
      v_bonus := rec.salary * 0.05;
    END IF;

    -- calculate tax
    CASE
      WHEN rec.salary < 3000 THEN
        v_tax := rec.salary * 0.1;
      WHEN rec.salary BETWEEN 3000 AND 7000 THEN
        v_tax := rec.salary * 0.2;
      ELSE
        v_tax := rec.salary * 0.3;
    END CASE;

    -- Aktualizacja danych pracownika
    UPDATE employees
    SET bonus = v_bonus, tax = v_tax
    WHERE employee_id = rec.employee_id;

  END LOOP;

  COMMIT;
END process_employee_data;

In the second example, we divide the logic into well-defined, reusable units and use them within the main procedure. All these components are encapsulated inside a package, which helps organize the code better, improve maintainability, and simplify future enhancements.

CREATE OR REPLACE PACKAGE employee_pkg IS
  -- Main procedure to process employee data
  PROCEDURE process_employee_data;
END employee_pkg;
/

CREATE OR REPLACE PACKAGE BODY employee_pkg IS

  -- Function to calculate bonus based on salary and department
  FUNCTION calculate_bonus(p_salary NUMBER, p_dept NUMBER) RETURN NUMBER IS
  BEGIN
    IF p_dept = 10 THEN
      RETURN p_salary * 0.1;
    ELSIF p_dept = 20 THEN
      RETURN p_salary * 0.15;
    ELSE
      RETURN p_salary * 0.05;
    END IF;
  END calculate_bonus;

  -- Function to calculate tax based on salary brackets
  FUNCTION calculate_tax(p_salary NUMBER) RETURN NUMBER IS
  BEGIN
    IF p_salary < 3000 THEN
      RETURN p_salary * 0.1;
    ELSIF p_salary BETWEEN 3000 AND 7000 THEN
      RETURN p_salary * 0.2;
    ELSE
      RETURN p_salary * 0.3;
    END IF;
  END calculate_tax;

  -- Procedure to update employee record with calculated bonus and tax
  PROCEDURE update_employee(p_employee_id NUMBER, p_bonus NUMBER, p_tax NUMBER) IS
  BEGIN
    UPDATE employees
    SET bonus = p_bonus,
        tax = p_tax
    WHERE employee_id = p_employee_id;
  END update_employee;

  -- Main procedure implementation: orchestrates the processing
  PROCEDURE process_employee_data IS
    v_bonus NUMBER;
    v_tax NUMBER;
  BEGIN
    FOR rec IN (SELECT employee_id, salary, department_id FROM employees) LOOP
      v_bonus := calculate_bonus(rec.salary, rec.department_id);
      v_tax := calculate_tax(rec.salary);
      update_employee(rec.employee_id, v_bonus, v_tax);
    END LOOP;

    COMMIT;
  END process_employee_data;

END employee_pkg;

Patterns and Best Practices

Layered Architecture

As a part of modular refactoring, applying a layered architecture is a well-established best practice. This approach divides code into clear, logical layers that separate responsibilities and improve readability, maintainability and testability. We can typically distinguish three main layers: validation, enrichment and processing layer.

Validation layer is responsible for verifying the correctness and completeness of input data. It ensures that only valid data proceeds to the next steps of processing, which allows to reduce errors and unexpected behavior. Validation layer is often implemented as set of dedicated procedures or functions, whose only purpose is to check that input values meet certain criteria before further processing. This might include checks as: verifying if customer exists, ensuring, that date is not in the future, checking if mandatory fields are populated. By separating business logic from validation you get cleaner and more readable code, easier unit testing of specific rules and better exception handling and error control. If data fails validation process, you can exit early or raise a specific exception without contaminating the core business logic.

Enrichment layer provides additional information or context required for further processing. This may include fetching related data or performing calculations needed. Here you can prepare full, standardized set of input data, fetching related data from other tables, auxiliary calculations, such as limits, priorities, VAT amount etc. For example: you can have only Customer ID, and enrichment layer can fetch all his data as age, credit history, calculate the customer’s credit score, determine risk level based on their history.

In simple case validation layer and enrichment layer can be combined. A well-structured SQL cursor can often filter-out invalid data and simultaneously join or compute related values. This “inline” approach is perfectly acceptable when logic is simple, performance matters and readability is not compromised. However, as complexity grows, keeping these responsibilities separated leads to cleaner, more maintainable code.

Processing layer is where business logic is executed. It operates on already validated and enriched data to perform calculations, update or insert records, generate reports or trigger downstream processes. This separation ensures that the processing logic is focused, predictable and easy to test - it doesn’t need to worry about data quality or context preparation.

Passing Records/Collections between procedures

When working with PL/SQL code, it is helpful to keep your procedures simple and easy to use. Instead of passing many individual parameters, you can group related data together using records or pass multiple rows at once using collections. Although this concept can seem a bit unnatural at the beginning, after some practice you will find it quite useful.

PL/SQL RECORD type let you combine related fields into a single data structure. This makes your procedure parameters easier to manage.

TYPE r_customer IS RECORD (
   id           NUMBER,
   name         VARCHAR2(100),
   date_of_birth DATE
);

PROCEDURE process_customer(p_customer IN r_customer);

As it is shown in the example above, instead of passing three separate parameters, you just pass one and it is clear, that all the values belong together.

You can deal with multiple rows in the similar way, using a collection of records. This can be very useful, when you processing lists of data, like orders, customers, transactions.

TYPE r_customer IS RECORD (
   id    NUMBER,
   name  VARCHAR2(100)
);

TYPE t_customer_tab IS TABLE OF r_customer INDEX BY PLS_INTEGER;

PROCEDURE validate_customers(p_customers IN t_customer_tab);
PROCEDURE enrich_customers(p_customers IN OUT t_customer_tab);

Using records and collections also ensures data consistency and improves performance. When you pass a single record or collection between procedures, you reduce the overhead of handling multiple individual parameters. You can enrich or validate the record within helper procedures and return it fully prepared for next step. This approach is more structured, simplifies the logic and reduces the room for errors.

From performance perspective, working with collections in bulk operations is much faster than row-by-row processing. Let’s see example of both approaches:

Traditional, row-by-row processing

FOR i IN 1 .. l_employees.COUNT LOOP
    INSERT INTO employees_copy (id, name, salary)
    VALUES (l_employees(i).id, l_employees(i).name, l_employees(i).salary);
END LOOP;

Optimized way, using FORALL

FORALL i IN 1 .. l_employees.COUNT
    INSERT INTO employees_copy (id, name, salary)
    VALUES l_employees(i);

Exception Handling Procedures

Effective exception handling is crucial for building reliable PL/SQL code. Unfortunately, it is often neglected in practice. As a result, errors are silently swallowed, leaving data in inconsistent state and developers are unaware that something went wrong. A common anti-pattern is using a generic WHEN OTHERS THEN clause without proper logging the error or meaningful error messages. This hides the root cause of issues and makes debugging extremely difficult. Another issue is lack of monitoring: even if a system logs errors into tables, no one may be reviewing those logs regularly. Error handling only works if paired with active error tracking and alerting.

Robust error exception handling isn’t just about catching errors - it is about making them visible, understandable and traceable.

Below is a common example of poor exception handling:

BEGIN
    -- Some processing logic
    UPDATE orders SET status = 'PROCESSED' WHERE order_id = l_order_id;

EXCEPTION
    WHEN OTHERS THEN
        -- Silent failure, no logging or context
        NULL;
END;

In that example we can see, that error is swallowed - we have no idea what went wrong. There is no logging, no propagation, no visibility. Bugs can silently corrupt data, leave the system in an unexpected state or be a cause of missing data.

Proper exception handling requires setting up meaningful logging table and creating helper procedures to log them where it is needed.

BEGIN
    -- Some processing logic
    UPDATE orders SET status = 'PROCESSED' WHERE order_id = l_order_id;

EXCEPTION
    WHEN OTHERS THEN
        -- Log the error for analysis
        log_error(
            p_module       => 'process_order',
            p_order_id     => l_order_id,
            p_error_code   => SQLCODE,
            p_error_msg    => SQLERRM,
            p_stack_trace  => DBMS_UTILITY.FORMAT_ERROR_BACKTRACE
        );
        -- Optionally re-raise to stop processing
        RAISE;
END;

This version, although still can be improved, is much better simply because it captures and stores meaningful context about the failure. It also makes debugging easier by recording the exact location and error message. And also - uses a centralized logging procedure, which you can define once and reuse in all procedures or functions, under the principle of modularity described at the beginning.

Ideally, we should handle not only WHEN OTHERS, but also specific exceptions like TOO_MANY_ROWS, NO_DATA_FOUND, DUP_VAL_ON_INDEX to improve clarity and control in error handling.

It is possible to even improve error handling, by using specific exceptions and passing contextual parameters. This allows you to track not only the fact that a failure occurred, but also the specific cause and the affected user or data entity. In that case error log become self-explanatory and it speeds up troubleshooting so much! You can even feed dashboards or monitoring systems with those data.

BEGIN
    -- Attempt to update customer data
    UPDATE customers
    SET bonus = calculate_bonus(p_customer_id)
    WHERE customer_id = p_customer_id;

EXCEPTION
    WHEN DUP_VAL_ON_INDEX THEN
        log_error(
            p_module       => 'update_bonus',
            p_context      => 'Duplicate customer ID: ' || p_customer_id,
            p_error_code   => SQLCODE,
            p_error_msg    => SQLERRM,
            p_stack_trace  => DBMS_UTILITY.FORMAT_ERROR_BACKTRACE
        );
        RAISE_APPLICATION_ERROR(-20010, 'Duplicate key for customer ID: ' || p_customer_id);

    WHEN NO_DATA_FOUND THEN
        log_error(
            p_module       => 'update_bonus',
            p_context      => 'Customer not found: ID = ' || p_customer_id,
            p_error_code   => SQLCODE,
            p_error_msg    => SQLERRM,
            p_stack_trace  => DBMS_UTILITY.FORMAT_ERROR_BACKTRACE
        );
        RAISE_APPLICATION_ERROR(-20011, 'Customer not found: ' || p_customer_id);

    WHEN OTHERS THEN
        log_error(
            p_module       => 'update_bonus',
            p_context      => 'Unexpected error for customer ID: ' || p_customer_id,
            p_error_code   => SQLCODE,
            p_error_msg    => SQLERRM,
            p_stack_trace  => DBMS_UTILITY.FORMAT_ERROR_BACKTRACE
        );
        RAISE;
END;

Procedure to log errors is quite easy to implement and it can be placed in shared utility package, such as TOOLS or COMMON. It’s also recommended to use an autonomous transaction, so the logging process is independent of the main transaction and won’t be rolled back if an error occurs.

PROCEDURE log_error (
    p_module      IN VARCHAR2,
    p_context     IN VARCHAR2,
    p_error_code  IN NUMBER,
    p_error_msg   IN VARCHAR2,
    p_stack_trace IN VARCHAR2
) IS
PRAGMA AUTONOMOUS_TRANSACTION;
BEGIN
    INSERT INTO error_log (
        log_date,
        module,
        context,
        error_code,
        error_message,
        stack_trace
    )
    VALUES (
        SYSDATE,
        p_module,
        p_context,
        p_error_code,
        p_error_msg,
        p_stack_trace
    );
    COMMIT; -- or leave it out, depending on your architecture
EXCEPTION
    WHEN OTHERS THEN
        -- Fallback if logging itself fails
        NULL;
END;

Effective error handling is essential for building reliable PL/SQL code, yet it is often treated as an afterthought. By catching specific exceptions and properly logging errors, with that in mind, you enable easier debugging and improve overall system resilience.

Testing Modular PL/SQL Code

One of the key advantages of modularizing PL/SQL code is that it becomes easier to test. When procedures and functions are small, focused and have clear inputs and outputs, they can be independently tested and verified.

Writing unit tests for procedures and functions

When writing unit test in PL/SQL, the goal is to isolate and verify one specific behavior at time. This involves a few key steps, that need to be followed. First of all, you need to prepare input data - for example inserting rows to stage table. Next - execute the function or procedure under test, verify the results and clean up any test data after.

When preparing test data remember to simulate different scenarios: typical inputs, edge cases and invalid data. You can use DBMS_OUTPUT to display expected vs. actual results for easier debugging. Add meaningful error messages and at least basic error handling to highlight mismatches when things go wrong. Consider keeping a simple log table to track results or anomalies. Save timestamps, input values, expected results and actual results. Remember to reset state of input data before next tests, to ensure consistency.

Group similar tests together: happy paths, edge cases and error scenarios.

A happy path refers to the expected, standard flow of execution where everything works correctly, no errors occur, and all input data is valid.
Edge cases involve testing boundary conditions or unusual inputs (e.g. salary = 0 or NULL).
Negative paths are scenarios where you intentionally feed bad data to test how the code handles errors.

And of course - when possible ;) - use test schemas or isolated environments to avoid side effects.

Let’s assume, that we are testing a procedure calculate bonus in hr_pkg, which returns 10% of salary as a bonus.

Here is the example of happy path - valid salary returns correct bonus:

DECLARE
   v_result NUMBER;
BEGIN
   v_result := hr_pkg.calculate_bonus(3000);

   IF v_result != 300 THEN
      raise_application_error(-20001, 'Test failed: Expected bonus = 300');
   END IF;

   dbms_output.put_line('Test passed: Happy path.');
END;

Now test with salary = 0 (boundary condition):

DECLARE
   v_result NUMBER;
BEGIN
   v_result := hr_pkg.calculate_bonus(0);

   IF v_result != 0 THEN
      raise_application_error(-20002, 'Test failed: Expected bonus = 0');
   END IF;

   dbms_output.put_line('Test passed: Edge case (salary = 0).');
END;

And negative path - test with invalid input (NULL) - it should raise an exception:

DECLARE
   v_result NUMBER;
BEGIN
   BEGIN
      v_result := hr_pkg.calculate_bonus(NULL);
      raise_application_error(-20003, 'Test failed: Expected exception on NULL input.');
   EXCEPTION
      WHEN VALUE_ERROR THEN
         dbms_output.put_line('Test passed: Caught VALUE_ERROR as expected.');
      WHEN OTHERS THEN
         dbms_output.put_line('Test failed: Unexpected exception - ' || SQLERRM);
   END;
END;

Also it is a good idea to check some business cases, like, for example, that bonus can’t exceed 1000 regardless of salary:

DECLARE
   v_result NUMBER;
BEGIN
   v_result := hr_pkg.calculate_bonus(20000); -- 10% of 20k = 2000, expect cap at 1000

   IF v_result != 1000 THEN
      raise_application_error(-20004, 'Test failed: Bonus should be capped at 1000');
   END IF;

   dbms_output.put_line('Test passed: Business rule cap enforced.');
END;

Automating tests with frameworks (utPLSQL or similar)

There are also tools like utPLSQL, a popular open-source testing framework for PL/SQL. While I haven’t used it extensively myself, it’s worth exploring for automating and organizing larger test suites, especially in more complex environments.

Real-world example

This is a single, overloaded procedure doing too much: calculating VAT, validating inputs, enriching customer data, and updating the final invoice table.

CREATE OR REPLACE PROCEDURE process_invoice (
   p_invoice_id IN NUMBER
) AS
   v_amount         NUMBER;
   v_vat            NUMBER;
   v_customer_name  VARCHAR2(100);
BEGIN
   -- Validate invoice exists
   SELECT amount INTO v_amount FROM invoices WHERE invoice_id = p_invoice_id;

   IF v_amount IS NULL THEN
      raise_application_error(-20001, 'Invoice amount is missing');
   END IF;

   -- Enrich data (get customer name)
   SELECT customer_name INTO v_customer_name
   FROM customers
   WHERE customer_id = (SELECT customer_id FROM invoices WHERE invoice_id = p_invoice_id);

   -- Calculate VAT
   v_vat := v_amount * 0.23;

   -- Update invoice with VAT and customer name
   UPDATE invoices
   SET vat_amount = v_vat,
       enriched_name = v_customer_name
   WHERE invoice_id = p_invoice_id;

   COMMIT;
EXCEPTION
   WHEN NO_DATA_FOUND THEN
      raise_application_error(-20002, 'Missing data');
   WHEN OTHERS THEN
      raise_application_error(-20099, 'Unexpected error: ' || SQLERRM);
END;

After refactoring, the code is broken into validation, enrichment, calculation, and processing layers inside a package. Each piece does one thing.

CREATE OR REPLACE PACKAGE invoice_pkg AS
   PROCEDURE process_invoice(p_invoice_id IN NUMBER);
   FUNCTION validate_invoice(p_invoice_id IN NUMBER) RETURN NUMBER;
   FUNCTION enrich_customer(p_invoice_id IN NUMBER) RETURN VARCHAR2;
   FUNCTION calculate_vat(p_amount IN NUMBER) RETURN NUMBER;
END invoice_pkg;
/

CREATE OR REPLACE PACKAGE BODY invoice_pkg AS

   FUNCTION validate_invoice(p_invoice_id IN NUMBER) RETURN NUMBER IS
      v_amount NUMBER;
   BEGIN
      SELECT amount INTO v_amount FROM invoices WHERE invoice_id = p_invoice_id;
      IF v_amount IS NULL THEN
         raise_application_error(-20001, 'Invoice amount is missing');
      END IF;
      RETURN v_amount;
   END;

   FUNCTION enrich_customer(p_invoice_id IN NUMBER) RETURN VARCHAR2 IS
      v_name VARCHAR2(100);
   BEGIN
      SELECT customer_name INTO v_name
      FROM customers
      WHERE customer_id = (SELECT customer_id FROM invoices WHERE invoice_id = p_invoice_id);
      RETURN v_name;
   END;

   FUNCTION calculate_vat(p_amount IN NUMBER) RETURN NUMBER IS
   BEGIN
      RETURN p_amount * 0.23;
   END;

   PROCEDURE process_invoice(p_invoice_id IN NUMBER) IS
      v_amount NUMBER;
      v_vat    NUMBER;
      v_name   VARCHAR2(100);
   BEGIN
      v_amount := validate_invoice(p_invoice_id);
      v_name   := enrich_customer(p_invoice_id);
      v_vat    := calculate_vat(v_amount);

      UPDATE invoices
      SET vat_amount = v_vat,
          enriched_name = v_name
      WHERE invoice_id = p_invoice_id;

      COMMIT;
   EXCEPTION
      WHEN NO_DATA_FOUND THEN
         raise_application_error(-20002, 'Missing data');
      WHEN OTHERS THEN
         raise_application_error(-20099, 'Unexpected error: ' || SQLERRM);
   END;
END invoice_pkg;

After refactoring, the procedure becomes significantly easier to read, maintain, and test. Instead of one large block handling validation, enrichment, calculation, and processing, we now have dedicated functions for each responsibility.

Each function now has a single purpose, following the Single Responsibility Principle, which makes the code clearer and reduces the risk of errors when making changes in the future. For example, if the VAT rate changes, we can update just the calculate_vat function without touching the rest of the logic.

Validation is handled separately, which means we can easily test and improve the checks without affecting other parts of the logic. Similarly, enrichment is handled by its own function, so if customer data retrieval changes (e.g., multiple sources or fallback logic), we can encapsulate it in one place.

Testability also improves - each function can now be tested in isolation. We can write unit tests for validate_invoice, calculate_vat, and enrich_customer without having to call the entire process_invoice. That also means better detection of bugs, as we can verify behavior in smaller, more manageable parts.

Error handling becomes clearer too. We're now raising meaningful, specific errors that make it easier to identify what went wrong. This helps both with debugging and logging in production environments.

In short, modular refactoring transforms a tightly coupled, hard-to-maintain procedure into clean, structured code that is easier to reason about, change, and trust.

Conclusion and Next Steps

Refactoring legacy PL/SQL code into smaller, modular components may seem like a daunting task, but the long-term benefits are well worth the effort. By applying modular design principles, you make your code more readable, testable, and maintainable. Smaller procedures with clear responsibilities reduce the risk of bugs, make enhancements easier to implement, and improve collaboration across development teams.

No matter how messy the starting point is, it is always possible to take the first step. Start small: pick a single procedure that’s difficult to understand or maintain and break it into logical parts. Wrap related logic in packages, introduce helper functions, and set up basic tests.

Refactoring is not just a technical activity - it’s an investment in the long-term health of your codebase and the sanity of every developer who has to work with it (including future you). It’s also an excellent learning opportunity: analyzing legacy code and restructuring it into clean, modular components sharpens your understanding of PL/SQL, improves your architectural thinking, and builds habits that strengthen your personal development as a developer.

So - choose one legacy procedure, open it up, and start refactoring. Your future projects will thank you.

Quickstart Refactoring Checklist

Pick a candidate - choose one difficult, bloated, or error-prone procedure from your legacy codebase.
Analyze the Logic - carefully read through the code. Identify mixed responsibilities, repeated logic, complex or nested conditions
Define logical units - break the procedure into smaller chunks, each responsible for a single task (validation, enrichment, processing, etc.).
Create helper procedures/functions - extract those chunks into smaller, named procedures or functions. Make sure each has a clear name, one purpose, and clean, minimal interface
Group with packages - organize related procedures and functions into packages. This improves encapsulation and reuse.
Add basic tests - write at least one test for the original behavior—happy path, edge case, and error scenario. Use DBMS_OUTPUT or logging if needed.
Improve exception handling - ensure specific errors are caught and logged clearly. Avoid WHEN OTHERS unless it’s used with proper logging and re-raising.
Review and document - document what was changed and why. Use comments to describe the purpose of each component.
Repeat incrementally - refactor one piece at a time. Over time, the whole codebase will become more modular and maintainable.

Thanks for reading - and good luck refactoring your PL/SQL! It might feel challenging at first, but with each improvement, your code becomes cleaner, more maintainable, and more enjoyable to work with. You’ll sharpen your skills, reduce bugs, and maybe even start to like those old legacy procedures :)

Taming Chaos in PL/SQL: Modular Refactoring of Legacy Procedures

Table of contents