Chapter 40: Testing, Quality Assurance, and Deployment -- Key Takeaways

Chapter Summary

Testing is the discipline that separates professional COBOL development from ad hoc coding, and this chapter covered the complete spectrum of testing, quality assurance, and deployment practices for COBOL programs. From unit testing individual paragraphs with frameworks like COBOL-Check, through integration testing of multi-program batch job streams, to regression testing that validates changes against established baselines, the chapter demonstrated that COBOL programs can and should be tested with the same rigor applied to programs in any modern language. The days of testing COBOL programs by submitting them to production and "seeing what happens" are long past; modern COBOL development demands automated, repeatable, and comprehensive testing at every level.

The chapter began with unit testing, explaining how COBOL-Check (an open-source unit testing framework for COBOL) allows programmers to write test cases that exercise individual paragraphs and verify their behavior in isolation. Test cases specify input values, execute the paragraph under test, and assert expected outcomes using a fluent, readable syntax. The chapter then covered test data management -- how to create, maintain, and version test data sets that provide predictable inputs for testing -- and the critical importance of testing boundary conditions, error paths, and edge cases rather than only the "happy path." Integration testing was presented in the context of multi-step batch job streams where the output of one program feeds into the next, requiring end-to-end validation of the complete processing chain.

Deployment and change management received equal attention, reflecting the reality that COBOL programs in production environments are subject to rigorous change control. The chapter covered the promotion path from development through testing, staging, and production environments, the role of change management systems in tracking and approving changes, and the emerging adoption of CI/CD (Continuous Integration/Continuous Delivery) pipelines for mainframe COBOL. Modern tools like IBM Dependency Based Build (DBB), Jenkins or Azure DevOps integration with mainframe build tools, and automated deployment through z/OS management facilities are transforming how COBOL programs move from a programmer's workstation to the production mainframe. The chapter emphasized that testing and deployment are not afterthoughts but integral parts of the development process that determine whether a program succeeds or fails in production.

Key Concepts

  • Unit testing in COBOL exercises individual paragraphs or sections in isolation, verifying that specific inputs produce expected outputs; frameworks like COBOL-Check enable this by inserting test code that runs within the COBOL program's own runtime environment.
  • COBOL-Check test cases use a readable syntax with EXPECT statements: MOVE 100 TO WS-BALANCE, PERFORM CALCULATE-INTEREST, EXPECT WS-INTEREST TO EQUAL 5.00 tests that the CALCULATE-INTEREST paragraph produces the correct result for a given balance.
  • Test data management involves creating controlled, reproducible data sets that cover normal cases, boundary conditions, error conditions, and edge cases; test data should be version-controlled alongside the program source code.
  • Boundary condition testing verifies program behavior at the limits of valid input: zero amounts, maximum field values, empty files, single-record files, date boundaries (month-end, year-end, leap year), and fields at their maximum length.
  • Error path testing verifies that the program handles invalid inputs, file I/O errors, arithmetic overflows, and other exceptional conditions correctly rather than abending or producing incorrect results.
  • Integration testing validates that multiple programs working together in a batch job stream produce correct end-to-end results, including verifying that intermediate files, database updates, and control totals are consistent across steps.
  • Regression testing compares the outputs of a modified program against the outputs of the original program using the same inputs, ensuring that changes have not introduced unintended side effects; automated comparison tools (file compare, DFSORT ICETOOL, custom compare programs) support this.
  • The change management process in production COBOL environments typically requires a formal change request, impact analysis, peer code review, testing evidence, management approval, and a back-out plan before any change is promoted to production.
  • CI/CD for mainframe COBOL automates the build-test-deploy pipeline: source code changes trigger automated compilation, unit test execution, integration test execution, and (upon approval) deployment to higher environments.
  • IBM Dependency Based Build (DBB) analyzes COBOL source code and copybook dependencies to determine which programs need recompilation when a copybook changes, enabling incremental builds that reduce compilation time.
  • Compile-time options affect program behavior and must be consistent across environments; options like ARITH(EXTEND) for extended arithmetic precision, TRUNC(OPT/BIN/STD) for binary field truncation, and SSRANGE for subscript range checking can cause programs to produce different results or abend differently.
  • Code review for COBOL programs should verify adherence to site standards, correct use of scope terminators, proper error handling, appropriate use of COMP-3 for monetary fields, and absence of common pitfalls (uninitialized fields, missing ON SIZE ERROR, hardcoded values).
  • Back-out planning ensures that if a production deployment causes problems, the previous version of all affected programs, copybooks, JCL, and data can be restored quickly; this requires version control and documented rollback procedures.
  • Test coverage metrics track which paragraphs, branches, and conditions have been exercised by the test suite, helping identify untested code paths that may harbor defects.

Common Pitfalls

  • Testing only the happy path: Programs that are tested only with valid, expected inputs will fail when they encounter production data with missing fields, invalid codes, extreme values, or unexpected combinations. Always test error paths and edge cases.
  • Using production data for testing without scrubbing: Production data contains personally identifiable information (PII), account numbers, and other sensitive data that must not be used in test environments without anonymization. Data masking tools can create realistic test data from production data while protecting privacy.
  • Not testing after copybook changes: Changing a copybook layout affects every program that copies it. Even if only one program was intentionally modified, all programs that include the changed copybook must be recompiled and regression-tested.
  • Skipping the back-out plan: Deploying to production without a tested back-out plan means that if the change causes problems, recovery depends on improvisation under pressure. Always have a documented, tested rollback procedure.
  • Inconsistent compiler options across environments: A program compiled with TRUNC(BIN) in development and TRUNC(STD) in production may produce different results for binary arithmetic. Ensure compiler options are identical in all environments.
  • Manual regression testing: Comparing outputs by visual inspection is slow, error-prone, and does not scale. Invest in automated file comparison tools and scripts that can detect differences in output files, reports, and database tables.
  • Treating deployment as a single step: Deployment of a COBOL change may require updating multiple programs, copybooks, JCL procedures, DB2 bind plans, CICS resource definitions, and operational procedures. Checklists and automation prevent missed steps.
  • Not versioning test cases: Test cases are code and should be version-controlled alongside the programs they test. Unversioned test cases become stale, unmaintained, and ultimately useless.

Quick Reference

      * COBOL-Check unit test example
      * File: CALCINT.cut (COBOL-Check test script)
      TESTSUITE 'Interest Calculation Tests'

      TESTCASE 'Simple interest at 5% on $1000'
          MOVE 1000.00  TO WS-BALANCE
          MOVE 0.05     TO WS-RATE
          MOVE 360      TO WS-DAYS-IN-YEAR
          MOVE 30       TO WS-DAYS
          PERFORM CALCULATE-INTEREST
          EXPECT WS-INTEREST TO EQUAL 4.17

      TESTCASE 'Zero balance produces zero interest'
          MOVE 0        TO WS-BALANCE
          MOVE 0.05     TO WS-RATE
          PERFORM CALCULATE-INTEREST
          EXPECT WS-INTEREST TO EQUAL 0

      TESTCASE 'Negative balance produces negative interest'
          MOVE -500.00  TO WS-BALANCE
          MOVE 0.05     TO WS-RATE
          MOVE 360      TO WS-DAYS-IN-YEAR
          MOVE 30       TO WS-DAYS
          PERFORM CALCULATE-INTEREST
          EXPECT WS-INTEREST TO BE NEGATIVE

      * Regression test comparison pattern
       COMPARE-OLD-AND-NEW.
           READ OLD-OUTPUT-FILE INTO WS-OLD-RECORD
               AT END SET WS-EOF-OLD TO TRUE
           END-READ
           READ NEW-OUTPUT-FILE INTO WS-NEW-RECORD
               AT END SET WS-EOF-NEW TO TRUE
           END-READ
           IF WS-OLD-RECORD NOT = WS-NEW-RECORD
               ADD 1 TO WS-DIFF-COUNT
               PERFORM WRITE-DIFFERENCE-REPORT
           END-IF
           .

      * Compile options for consistent behavior
      *   CBL ARITH(EXTEND),TRUNC(STD),SSRANGE
      *       NUMPROC(NOPFD),TEST(DWARF,SOURCE)

      * JCL for automated test execution
      //UNITTEST EXEC PGM=COBOLCHK,
      //         PARM='TESTPGM=CALCINT'
      //STEPLIB  DD DSN=TEST.LOADLIB,DISP=SHR
      //SYSPRINT DD SYSOUT=*
      //TESTDATA DD DSN=TEST.DATA.CALCINT,DISP=SHR

      * CI/CD pipeline steps (conceptual)
      *  1. Developer commits to Git branch
      *  2. Jenkins triggers DBB build
      *  3. DBB compiles changed programs + dependents
      *  4. COBOL-Check unit tests execute
      *  5. Integration test job stream runs
      *  6. Results compared to baseline
      *  7. If all pass -> promote to staging
      *  8. After approval -> deploy to production

What's Next

Chapter 41 brings everything together in a capstone project: a complete banking application that integrates batch processing, CICS online transactions, and DB2 database access. You will design, implement, test, and deploy a multi-program system that applies the financial calculations, transaction processing, testing, and deployment practices from the preceding chapters. The capstone project demonstrates how all the individual skills you have learned combine into a functioning enterprise application.