COBOL Tutorial

Data Movement Operations

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MOVE Statement Basics

The MOVE statement is the most fundamental data movement operation in COBOL. It transfers data from one field to another, automatically handling any necessary data type conversions according to COBOL's conversion rules.

Basic Syntax

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MOVE {identifier-1|literal-1} TO identifier-2 [identifier-3 ...]

This format moves the contents of a source field or literal to one or more receiving fields.

MOVE Statement Examples

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* Example 1: Move a literal to a field
MOVE "JOHN DOE" TO CUSTOMER-NAME.
 
* Example 2: Move one field to another
MOVE ACCOUNT-NUMBER TO OUTPUT-ACCOUNT.
 
* Example 3: Move to multiple fields
MOVE SPACES TO FIRST-NAME, LAST-NAME, MIDDLE-INITIAL.
 
* Example 4: Move a numeric literal
MOVE 100.50 TO ITEM-PRICE.
 
* Example 5: Move figurative constants
MOVE ZEROS TO QUANTITY-ON-HAND, REORDER-POINT.
MOVE HIGH-VALUES TO END-OF-TABLE-MARKER.
MOVE LOW-VALUES TO BINARY-SEARCH-START.

Data Type Conversion Rules

Numeric to Numeric: Decimal points are aligned; truncation or zero padding may occur
Alphanumeric to Alphanumeric: Left-justified with space padding or right truncation
Numeric to Alphanumeric: Numeric value is converted to display format
Alphanumeric to Numeric: Data must contain valid digits; spaces are treated as zeros
Group Items: Moved as a single alphanumeric field regardless of content

Potential Issues with MOVE

Truncation: When the receiving field is smaller than the sending field
Data corruption: When moving non-numeric data to numeric fields
Sign loss: When moving signed numbers to unsigned fields
Precision loss: When decimal positions are lost in numeric conversions

Always ensure that receiving fields have appropriate sizes and data types for the expected data.

MOVE CORRESPONDING Statement

The MOVE CORRESPONDING (or MOVE CORR) statement transfers data between group items by matching field names at any level. This powerful feature allows for selective copying of fields without explicitly naming each one.

MOVE CORRESPONDING Syntax

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MOVE CORRESPONDING|CORR identifier-1 TO identifier-2

Both identifiers must be group items. Only elementary items with matching names will be moved.

Data Structure Example

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* Source and target record structures
01 INPUT-RECORD.
   05 CUSTOMER-ID       PIC X(10).
   05 CUSTOMER-NAME     PIC X(30).
   05 ACCOUNT-NUMBER    PIC X(15).
   05 TRANSACTION-DATE  PIC X(10).
   05 TRANSACTION-AMT   PIC S9(7)V99.
 
01 OUTPUT-RECORD.
   05 ORDER-NUMBER      PIC X(12).
   05 CUSTOMER-ID       PIC X(10).
   05 CUSTOMER-NAME     PIC X(30).
   05 ACCOUNT-NUMBER    PIC X(15).
   05 PAYMENT-METHOD    PIC X(10).

MOVE CORRESPONDING Example

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* Using MOVE CORRESPONDING
MOVE CORRESPONDING INPUT-RECORD TO OUTPUT-RECORD.
 
* This is equivalent to writing:
MOVE INPUT-RECORD-CUSTOMER-ID TO OUTPUT-RECORD-CUSTOMER-ID.
MOVE INPUT-RECORD-CUSTOMER-NAME TO OUTPUT-RECORD-CUSTOMER-NAME.
MOVE INPUT-RECORD-ACCOUNT-NUMBER TO OUTPUT-RECORD-ACCOUNT-NUMBER.
 
* Note that TRANSACTION-DATE and TRANSACTION-AMT are not moved 
* because OUTPUT-RECORD does not have fields with those names.
* Similarly, ORDER-NUMBER and PAYMENT-METHOD are not affected.

Qualification Rules

Only elementary items (not group items) with identical names are moved
Items must have the same qualified name (all parent levels must match)
The REDEFINES, RENAMES, and OCCURS clauses do not affect name matching
Receiving fields with JUSTIFIED RIGHT are moved according to standard rules
Level numbers do not need to match, only names matter

Best Practices for MOVE CORRESPONDING

Use for copying between structures with many matching fields
Be cautious when field definitions change - MOVE CORRESPONDING silently adjusts
Consider explicit moves when traceability is more important than brevity
Document field mappings when using MOVE CORRESPONDING for clarity
Check for unintended side effects when structures evolve over time

INITIALIZE Statement

The INITIALIZE statement is a powerful tool for setting fields to their default values or to specific values. It's especially useful for clearing large data structures with a single statement.

Basic Syntax

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INITIALIZE identifier-1 [identifier-2 ...]
    [REPLACING 
         {ALPHABETIC|ALPHANUMERIC|NUMERIC|ALPHANUMERIC-EDITED|NUMERIC-EDITED}
         DATA BY {identifier-3|literal-1}] ...

Without the REPLACING phrase, numeric items are set to zero and non-numeric items to spaces.

Basic INITIALIZE Examples

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* Example 1: Simple initialization
INITIALIZE CUSTOMER-RECORD.
 
* Example 2: Initialize multiple items
INITIALIZE CUSTOMER-NAME, CUSTOMER-ADDRESS, CUSTOMER-PHONE.
 
* Example 3: Initialize a table (all elements are set)
INITIALIZE PRODUCT-TABLE.

Advanced INITIALIZE with REPLACING

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* Example 1: Set all numeric fields to a specific value
INITIALIZE EMPLOYEE-RECORD
    REPLACING NUMERIC DATA BY 9999.
 
* Example 2: Set multiple categories with different values
INITIALIZE TRANSACTION-RECORD
    REPLACING NUMERIC DATA BY ZERO
              ALPHANUMERIC DATA BY "PENDING"
              ALPHANUMERIC-EDITED DATA BY SPACES.
 
* Example 3: Initialize with values from other fields
INITIALIZE CUSTOMER-RECORD
    REPLACING ALPHANUMERIC DATA BY DEFAULT-CUSTOMER-NAME
              NUMERIC DATA BY DEFAULT-CREDIT-LIMIT.

INITIALIZE Behavior Rules

Only elementary items are initialized, not the group items themselves
If the identifier is a group item, all elementary items within it are initialized
OCCURS clauses are fully processed - all table elements are initialized
REDEFINES areas are all processed - redefined items are initialized
Data items with BLANK WHEN ZERO are handled appropriately
88-level condition items are not directly affected, but their values may change

INITIALIZE vs. Multiple MOVEs

Feature	INITIALIZE	Multiple MOVEs
Code brevity	More concise	More verbose
Execution speed	May be slower	May be faster
Handling complex structures	Handles automatically	Requires more code
Data type sensitivity	Automatically handles each data type	Manual control needed
Selective initialization	REPLACING option allows type selection	Total control over what gets initialized

STRING Operations

The STRING statement concatenates multiple fields or literals into a single field, providing powerful string manipulation capabilities for building complex text strings.

Basic STRING Syntax

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STRING {identifier-1|literal-1} [DELIMITED BY {identifier-2|literal-2|SIZE}]
       [{identifier-3|literal-3} [DELIMITED BY {identifier-4|literal-4|SIZE}]] ...
    INTO identifier-5
    [WITH POINTER identifier-6]
    [ON OVERFLOW imperative-statement-1]
    [NOT ON OVERFLOW imperative-statement-2]
    [END-STRING]

The STRING statement combines multiple source fields or literals into a single receiving field.

Key Components

DELIMITED BY: Specifies where to stop copying from the sending field
SIZE: Uses the entire sending field
INTO: Specifies the receiving field
WITH POINTER: Specifies where to start placing data in the receiving field
ON OVERFLOW: Handles cases when the receiving field is too small

Basic STRING Examples

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* Example 1: Combining fields with spaces
STRING FIRST-NAME DELIMITED BY SIZE
       SPACE DELIMITED BY SIZE
       LAST-NAME DELIMITED BY SIZE
    INTO FULL-NAME.
 
* Example 2: Using specific delimiters
STRING AREA-CODE DELIMITED BY SIZE
       "-" DELIMITED BY SIZE
       PREFIX DELIMITED BY SIZE
       "-" DELIMITED BY SIZE
       LINE-NUMBER DELIMITED BY SIZE
    INTO FORMATTED-PHONE.
 
* Example 3: Delimiting by a character
STRING PRODUCT-CODE DELIMITED BY "/"
       CATEGORY-CODE DELIMITED BY SPACE
    INTO OUTPUT-CODE.

Advanced STRING Features

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* Example 1: Using the POINTER phrase
MOVE 1 TO STRING-PTR.
STRING PREFIX DELIMITED BY SIZE
       ROOT-WORD DELIMITED BY SIZE
       SUFFIX DELIMITED BY SIZE
    INTO COMPOUND-WORD
    WITH POINTER STRING-PTR.
 
* Example 2: Handling overflow
STRING FIELD-1 DELIMITED BY SIZE
       FIELD-2 DELIMITED BY SIZE
       FIELD-3 DELIMITED BY SIZE
    INTO SHORT-OUTPUT
    ON OVERFLOW
       DISPLAY "Output field too small"
       PERFORM ERROR-ROUTINE
    NOT ON OVERFLOW
       DISPLAY "String operation successful"
END-STRING.
 
* Example 3: Building a CSV line
STRING ID-NUMBER DELIMITED BY SIZE
       "," DELIMITED BY SIZE
       CUSTOMER-NAME DELIMITED BY ","
       "," DELIMITED BY SIZE
       TRANSACTION-AMT DELIMITED BY SIZE
    INTO CSV-LINE.

STRING Execution Flow

If POINTER is specified, its value determines the starting position in the receiving field
Each sending field is processed from left to right
Characters are copied until the delimiter is encountered or the entire field is copied (with SIZE)
The pointer value is automatically incremented for each character moved
If the receiving field becomes full, the ON OVERFLOW condition triggers
After all sending fields are processed, the NOT ON OVERFLOW condition triggers (if no overflow)
The final pointer value is available for subsequent operations

UNSTRING Operations

The UNSTRING statement splits a string field into multiple fields based on specified delimiters. It's the complement to STRING and is particularly useful for parsing formatted input data.

Basic UNSTRING Syntax

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UNSTRING identifier-1
    [DELIMITED BY [ALL] {identifier-2|literal-1} [OR [ALL] {identifier-3|literal-2}] ...]
    INTO {identifier-4 [DELIMITER IN identifier-5] [COUNT IN identifier-6]}
         [{identifier-7 [DELIMITER IN identifier-8] [COUNT IN identifier-9]}] ...
    [WITH POINTER identifier-10]
    [TALLYING IN identifier-11]
    [ON OVERFLOW imperative-statement-1]
    [NOT ON OVERFLOW imperative-statement-2]
    [END-UNSTRING]

The UNSTRING statement parses data from the sending field into multiple receiving fields.

Key Components

DELIMITED BY: Defines what separates the fields in the source string
ALL: Treats consecutive delimiters as one delimiter
INTO: Specifies the receiving fields
DELIMITER IN: Captures the actual delimiter found
COUNT IN: Records the number of characters moved to each field
WITH POINTER: Specifies where to start scanning in the sending field
TALLYING IN: Counts the number of fields processed

Basic UNSTRING Examples

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* Example 1: Parse a name (First Last)
UNSTRING FULL-NAME
    DELIMITED BY SPACE
    INTO FIRST-NAME
         LAST-NAME.
 
* Example 2: Parse a comma-separated value
UNSTRING CSV-RECORD
    DELIMITED BY ","
    INTO FIELD-1
         FIELD-2
         FIELD-3
         FIELD-4.
 
* Example 3: Multiple delimiter options
UNSTRING ADDRESS-LINE
    DELIMITED BY "," OR ";" OR SPACE
    INTO STREET-NUMBER
         STREET-NAME
         APARTMENT-NUMBER.

Advanced UNSTRING Features

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* Example 1: Using DELIMITER IN and COUNT IN
UNSTRING SOURCE-FIELD
    DELIMITED BY "," OR ":" OR ";"
    INTO FIELD-1 DELIMITER IN DELIM-1 COUNT IN COUNT-1
         FIELD-2 DELIMITER IN DELIM-2 COUNT IN COUNT-2
         FIELD-3 DELIMITER IN DELIM-3 COUNT IN COUNT-3.
 
* Example 2: Using POINTER and TALLYING
MOVE 1 TO UNSTR-PTR.
MOVE 0 TO FIELD-COUNT.
UNSTRING SOURCE-STRING
    DELIMITED BY ALL SPACES
    INTO FIELD-1
         FIELD-2
         FIELD-3
         FIELD-4
         FIELD-5
    WITH POINTER UNSTR-PTR
    TALLYING IN FIELD-COUNT
    ON OVERFLOW
        DISPLAY "Too many fields in input"
END-UNSTRING.
 
* Example 3: Parsing a date (MM/DD/YYYY)
UNSTRING DATE-STRING
    DELIMITED BY "/"
    INTO MONTH-FIELD
         DAY-FIELD
         YEAR-FIELD.

Practical Example: CSV Parsing

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* Example of parsing a complex CSV record
* Input: "101,"Smith, John",Active,10000.50"
 
UNSTRING CSV-RECORD
    DELIMITED BY "," OR ALL SPACES
    INTO CUSTOMER-ID
         TEMP-NAME DELIMITER IN DELIM-1 COUNT IN NAME-LENGTH
         STATUS-FIELD
         BALANCE-FIELD
    WITH POINTER CSV-POINTER
    TALLYING IN FIELD-COUNT
    ON OVERFLOW
        PERFORM CSV-OVERFLOW-HANDLING
    NOT ON OVERFLOW
        PERFORM RECORD-PROCESSING
END-UNSTRING.
 
* Note: This example would need additional processing for the
* quoted field "Smith, John" which contains a comma.

Complex data parsing often requires multiple steps, including pre-processing for quoted fields or other special cases.

Data Conversion Considerations

COBOL performs automatic data type conversions during data movement operations. Understanding these conversions is crucial for writing reliable programs that handle data correctly.

Numeric to Numeric Conversions

Decimal points are aligned during conversion
Truncation occurs if the receiving field has fewer digits
Zeros are padded if the receiving field has more digits
Sign is preserved if the receiving field is signed
Sign is lost if moving from signed to unsigned field
Different USAGE types (COMP, DISPLAY, etc.) are automatically converted

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* Examples of numeric to numeric conversions
* Receiving field: PIC 9(4)
MOVE 123 TO NUMERIC-FIELD.     * Result: 0123
MOVE 12345 TO NUMERIC-FIELD.   * Result: 2345 (truncated)
 
* Receiving field: PIC S9(4)
MOVE -123 TO SIGNED-FIELD.     * Result: -0123
MOVE 12345 TO SIGNED-FIELD.    * Result: +2345 (truncated)
 
* Receiving field: PIC 9(4)V99
MOVE 123.456 TO DECIMAL-FIELD. * Result: 0123.45 (truncated)
MOVE 123 TO DECIMAL-FIELD.     * Result: 0123.00 (padded)

Alphanumeric to Alphanumeric Conversions

Data is left-justified in the receiving field
If the receiving field is longer, spaces are padded on the right
If the receiving field is shorter, the data is truncated from the right
JUSTIFIED RIGHT clause affects the alignment of data (rare in COBOL)

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* Examples of alphanumeric to alphanumeric conversions
* Receiving field: PIC X(10)
MOVE "ABC" TO ALPHA-FIELD.      * Result: "ABC       " (padded)
MOVE "ABCDEFGHIJKL" TO ALPHA-FIELD. * Result: "ABCDEFGHIJ" (truncated)
 
* Receiving field: PIC X(10) JUSTIFIED RIGHT
MOVE "ABC" TO JUSTIFIED-FIELD.  * Result: "       ABC" (right-justified)

Numeric to Alphanumeric Conversions

The numeric value is converted to its display representation
Leading zeros are included in the conversion
Signs are included in the conversion if present
Decimal points are included in the conversion if present
Standard left-justification and potential truncation applies

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* Examples of numeric to alphanumeric conversions
* Numeric field: PIC 9(4)V99 VALUE 123.45
* Receiving field: PIC X(10)
MOVE NUMERIC-FIELD TO ALPHA-FIELD.  * Result: "0123.45   " (left-justified)
 
* Numeric field: PIC S9(4) VALUE -123
* Receiving field: PIC X(10)
MOVE SIGNED-FIELD TO ALPHA-FIELD.   * Result: "-0123     " (sign included)
 
* Numeric field: PIC 9(6) VALUE 123456
* Receiving field: PIC X(4)
MOVE LARGE-NUM TO SMALL-ALPHA.      * Result: "1234" (truncated)

Alphanumeric to Numeric Conversions

The alphanumeric data must contain valid numeric characters
Leading and trailing spaces are treated as zeros
The data is aligned by the decimal point, if present
If the data contains invalid characters, runtime errors may occur
It's recommended to validate alphanumeric data before conversion

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* Examples of alphanumeric to numeric conversions
* Alphanumeric field: PIC X(10) VALUE "123"
* Receiving field: PIC 9(5)
MOVE ALPHA-FIELD TO NUMERIC-FIELD.  * Result: 00123
 
* Alphanumeric field: PIC X(10) VALUE "   123   "
* Receiving field: PIC 9(5)
MOVE ALPHA-FIELD TO NUMERIC-FIELD.  * Result: 00123 (spaces treated as zeros)
 
* Alphanumeric field: PIC X(10) VALUE "12.34"
* Receiving field: PIC 9(3)V99
MOVE ALPHA-FIELD TO DECIMAL-FIELD.  * Result: 012.34
 
* Alphanumeric field: PIC X(10) VALUE "ABC123"
* Receiving field: PIC 9(5)
MOVE ALPHA-FIELD TO NUMERIC-FIELD.  * Runtime error or unpredictable result

Best Practices for Data Conversion

Always validate data before conversion, especially alphanumeric to numeric
Use appropriate field sizes to prevent truncation
Consider the ROUNDED phrase for numeric operations to maintain precision
Be aware of sign handling when moving between signed and unsigned fields
Use explicit data validation with IF...NUMERIC conditions before conversion
Document data conversion assumptions and requirements in your code
Test edge cases, including maximum and minimum values

Exercises: Data Movement Operations

Exercise 1: Customer Record Processing

Write COBOL statements to convert a raw customer input record to a formatted output record.

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* Given these data structures:
01 RAW-CUSTOMER-RECORD.
   05 RAW-CUSTOMER-ID     PIC X(8).
   05 RAW-CUSTOMER-NAME   PIC X(30).
   05 RAW-PHONE-NUMBER    PIC X(10).
   05 RAW-ADDRESS         PIC X(50).
   05 RAW-CREDIT-LIMIT    PIC X(8).
 
01 FORMATTED-CUSTOMER.
   05 FORMATTED-ID        PIC X(8).
   05 FORMATTED-NAME      PIC X(30).
   05 FORMATTED-PHONE.
      10 AREA-CODE        PIC X(3).
      10 FILLER           PIC X VALUE "-".
      10 PREFIX           PIC X(3).
      10 FILLER           PIC X VALUE "-".
      10 LINE-NUMBER      PIC X(4).
   05 FORMATTED-CREDIT    PIC 9(6)V99.
 
* Your task:
* 1. Move the customer ID and name fields appropriately
* 2. Parse the phone number into its components using UNSTRING
* 3. Convert the credit limit from character to numeric format
* 4. Write error handling for invalid numeric data

Exercise 2: Address Formatting

Use STRING operations to create a properly formatted mailing address from component fields.

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* Given these data structures:
01 ADDRESS-COMPONENTS.
   05 STREET-NUMBER    PIC X(10).
   05 STREET-NAME      PIC X(20).
   05 APARTMENT        PIC X(10).
   05 CITY             PIC X(20).
   05 STATE            PIC XX.
   05 ZIP-CODE         PIC X(10).
 
01 FORMATTED-ADDRESS.
   05 ADDRESS-LINE-1   PIC X(40).
   05 ADDRESS-LINE-2   PIC X(40).
   05 CITY-STATE-ZIP   PIC X(40).
 
* Your task:
* 1. Format ADDRESS-LINE-1 with the street number and name
* 2. Format ADDRESS-LINE-2 with apartment info (if non-blank)
* 3. Format CITY-STATE-ZIP as "CITY, STATE ZIP-CODE"
* 4. Use STRING statements with appropriate delimiters
* 5. Include error handling for potential overflow

Exercise 3: Initializing Complex Records

Use the INITIALIZE statement to efficiently reset fields in a complex data structure.

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* Given this complex record structure:
01 TRANSACTION-RECORD.
   05 TRANSACTION-HEADER.
      10 TRANS-TYPE        PIC X.
         88 SALE           VALUE "S".
         88 RETURN         VALUE "R".
         88 ADJUSTMENT     VALUE "A".
      10 TRANS-DATE        PIC X(8).
      10 TRANS-TIME        PIC X(6).
      10 REGISTER-NUMBER   PIC 9(3).
      10 EMPLOYEE-ID       PIC X(5).
   05 TRANSACTION-DETAILS.
      10 ITEM-COUNT        PIC 9(3).
      10 ITEMS OCCURS 0 TO 50 TIMES DEPENDING ON ITEM-COUNT.
         15 ITEM-ID        PIC X(10).
         15 ITEM-QUANTITY  PIC 9(3).
         15 ITEM-PRICE     PIC 9(5)V99.
         15 ITEM-DISCOUNT  PIC 9(3)V99.
   05 TRANSACTION-TOTALS.
      10 SUBTOTAL          PIC 9(7)V99.
      10 TAX-AMOUNT        PIC 9(5)V99.
      10 TOTAL-AMOUNT      PIC 9(7)V99.
 
* Your task:
* 1. Write INITIALIZE statements to reset this record between transactions
* 2. Keep certain header fields (like REGISTER-NUMBER) unchanged
* 3. Reset all numeric amounts to zero
* 4. Reset all item identifiers to spaces
* 5. Use the REPLACING phrase where appropriate

Frequently Asked Questions

Test Your Knowledge

1. What happens when a numeric field is moved to an alphanumeric field?

The operation fails with a compilation error
The numeric value is converted to its display representation
The field is filled with spaces
A runtime error occurs

2. What does MOVE CORRESPONDING do?

Moves all fields from one group item to another
Moves only fields with matching names from one group item to another
Moves items in the corresponding order
Automatically handles data type conversions

3. In STRING operation, what does the POINTER phrase do?

Points to the next field to be processed
Specifies where to begin storing characters in the receiving field
Contains the address of the field in memory
Points to the delimiter character

4. What does the INITIALIZE statement do by default?

Sets numeric items to zeros and non-numeric items to spaces
Sets all items to their default values as in WORKING-STORAGE
Sets all items to null
Resets all items to zero

5. What is a key difference between STRING and UNSTRING operations?

STRING requires a fixed-length field while UNSTRING can use variable-length fields
STRING combines multiple sending fields into one receiving field, while UNSTRING splits one sending field into multiple receiving fields
STRING is for numeric data, UNSTRING is for alphanumeric data
STRING is a legacy operation, UNSTRING is the modern replacement

Basic Arithmetic Operations Procedure Branching

COBOL Tutorial

Data Movement Operations

MOVE Statement Basics

Basic Syntax

MOVE Statement Examples

Data Type Conversion Rules

Potential Issues with MOVE

MOVE CORRESPONDING Statement

MOVE CORRESPONDING Syntax

Data Structure Example

MOVE CORRESPONDING Example

Qualification Rules

Best Practices for MOVE CORRESPONDING

INITIALIZE Statement

Basic Syntax

Basic INITIALIZE Examples

Advanced INITIALIZE with REPLACING

INITIALIZE Behavior Rules

INITIALIZE vs. Multiple MOVEs

STRING Operations

Basic STRING Syntax

Key Components

Basic STRING Examples

Advanced STRING Features

STRING Execution Flow

UNSTRING Operations

Basic UNSTRING Syntax

Key Components

Basic UNSTRING Examples

Advanced UNSTRING Features

Practical Example: CSV Parsing

Data Conversion Considerations

Numeric to Numeric Conversions

Alphanumeric to Alphanumeric Conversions

Numeric to Alphanumeric Conversions

Alphanumeric to Numeric Conversions

Best Practices for Data Conversion

Exercises: Data Movement Operations

Exercise 1: Customer Record Processing

Exercise 2: Address Formatting

Exercise 3: Initializing Complex Records

Frequently Asked Questions

When should I use MOVE CORRESPONDING instead of individual MOVE statements?

What happens if the receiving field in a MOVE operation is smaller than the sending field?

How do STRING and UNSTRING handle field overflow?

What's the difference between INITIALIZE and MOVE statements for clearing fields?

How can I handle data type conversions safely in COBOL?

Related Pages

Related Concepts

Data Type Conversion

Group vs. Elementary Items

PICTURE Clause

INSPECT Statement

COBOL Reference Modification

Test Your Knowledge