Case Study: Modernizing Legacy COBOL Batch Processing for a Leading Food Wholesaler

Keyhole Software successfully modernized a legacy COBOL batch processing system, addressing inefficiencies and integration challenges through an AI-optimized POC. Keyhole accelerated development efforts by 20-30% leveraging AI-driven tools in the migration from COBOL to Spring Batch, enhancing scalability, maintainability, and cloud-native capabilities for the client’s system.

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Client Highlight

The client is a prominent cooperative in the wholesale food distribution industry, providing a wide range of products to independent retailers and larger retail chains. Their operations encompass a comprehensive network of inventory management, logistics, and reclamation processing, all designed to meet the needs of their extensive customer base.

Key Technologies

• COBOL – Legacy Batch Processing System
• Spring Batch – Modern Batch Processing Framework
• Spring Cloud Data Flow (SCDF) – Microservices-Based Toolkit for Batch Processing Management
• Docker – Containerization
• Kubernetes – Container Orchestration
• EnterpriseGPT – AI-Driven Tool for COBOL Code Analysis and Migration
• Oracle PeopleSoft – Integration for Reclamation Processing
• FTP – File Transfer Protocol for Input File Retrieval

Challenge: Outdated COBOL Batch System Limiting Efficiency

Keyhole’s client faced the challenge of modernizing its outdated COBOL batch processing system, which was hindering efficiency, maintainability, and integration with modern platforms. With a range of legacy systems in place, the client sought a strategic approach to update and integrate these systems.

Keyhole Software proposed a targeted Proof of Concept (POC) using a single mainframe-based batch processing application as a starting point and migrating it to modern batch processing, leveraging an AI Assist Framework, to streamline future modernization efforts across other applications within the organization.

Proof of Concept Focus: A Critical Job for Reclamation Processing

The initial POC centered on a specific application responsible for processing reclamation data from customer stores and importing it into an Oracle PeopleSoft system. This involved:

• Retrieving input files via FTP
• Performing complex calculations
• Generating output files for PeopleSoft integration

Given the challenges associated with maintaining legacy COBOL-based systems, the client partnered with Keyhole Software to explore strategies for modernization that could be replicated across other systems. This COBOL batch job was reimplemented as part of this POC as a Spring Batch application.

Keyhole’s Solution: AI-Accelerated COBOL Modernization

Proof of Concept (POC) with Spring Batch

Keyhole Software converted the batch job into a modern, cloud-ready Spring Batch application. Spring Batch provides:

• Chunk-Based Processing: reads, processes, & writes data in chunks for better performance.
• Step-Oriented Workflow: breaks jobs into modular steps for flexibility and reusability.
• Retry and Skip Logic: auto-retry failures and skipping faulty records.
• Job Monitoring & Restartability: tracking execution & restarting from failures.

Cloud-Native Orchestration with SCDF

Keyhole integrated Spring Cloud Data Flow (SCDF), a microservices-based toolkit for managing batch processing pipelines to enhance batch process management. SCDF enabled:

• Declarative pipeline management
• Fault tolerance & scalability
• Seamless integration with cloud-native architectures

AI-Driven Acceleration with EnterpriseGPT

Keyhole leveraged EnterpriseGPT, a Keyhole Software developed, AI-powered chatbot built to interact with OpenAPI in a private manner. This proprietary tool was specifically developed to ensure that any submitted data is not retained or used to train the OpenAPI LLM model, maintaining strict confidentiality for enterprise applications.

EnterpriseGPT’s Capabilities

EnterpriseGPT streamlines COBOL-to-Spring Batch modernization by automating key development tasks and reducing manual effort.

Seamless Large File Handling

Unlike traditional methods requiring cumbersome copy-pasting, EnterpriseGPT allows users to attach COBOL source files directly within chat sessions. Consultants can then prompt the AI to analyze and explain job functions, generating clear, AI-driven documentation of legacy code structures.

Automated Spring Batch Development

Beyond analysis, EnterpriseGPT actively assists in building Spring Batch applications. With prompts like “Convert this COBOL job into a Spring Batch application,” the AI generates an initial job skeleton, accelerating the development process.

Key AI-Generated Components:

• Batch Job Structure – Defines processing steps for streamlined execution.
• Optimized Readers, Writers & Processors – Suggests efficient data-handling components.
• COBOL-to-Java Transformation – Converts working storage into Java POJOs.
• Data Definition Equivalents – Maps COBOL structures to Java-based configurations.

Developers can refine AI-generated output iteratively, leveraging automation while ensuring best practices in modernization.

EnterpriseGPT’s Impact on Efficiency

By leveraging EnterpriseGPT, the modernization effort saw a 20–30% reduction in manual effort. This acceleration was achieved through:

• AI-assisted COBOL code analysis and explanation
• Automated generation of Spring Batch configuration code
• AI-driven transformation of working storage sections into Java POJOs

While expert knowledge in Java, Spring, and Spring Batch remains essential, EnterpriseGPT’s assistance allows developers to focus on fine-tuning business logic rather than spending excessive time on rote code translation.

Implementation & Deployment

The modernized Spring Batch application was:

• Containerized with Docker
• Deployed on Kubernetes
• Integrated with SCDF for scalable execution

The entire conversion process took approximately 16 hours, with AI assistance significantly boosting productivity. As developers gain familiarity with COBOL-to-Java transformations, future projects are expected to achieve even greater efficiency gains.

Results: A Scalable & Future-Ready Batch Processing System

The successful POC demonstrated that COBOL batch processes can be efficiently modernized with Spring Batch & AI-driven tools. Benefits include:

• Improved Scalability & Maintainability – Modern Java-based architecture simplifies support & future enhancements.
• Cloud-Native Flexibility – SCDF-enabled orchestration ensures seamless batch job execution in the cloud.
• AI-Powered Acceleration – 20–30% faster development with EnterpriseGPT-assisted code migration.

The final Spring Batch implementation, hosted in a private GitHub repository, serves as a repeatable template for future COBOL modernization efforts.

Looking to Modernize Your Legacy Systems?

Keyhole Software specializes in legacy system modernization, AI-assisted migration, and cloud-native transformations. Our expertise in Spring Batch, SCDF, and AI-driven tools ensures efficient, scalable modernization. Get in touch today to see how we can help accelerate your transformation.