COBOL to Java Migration Services

Through parallel-run regression testing. The original COBOL system and the new Java application are executed against the same input data, and outputs are compared at the byte level, including calculations, logs, file generation, database updates, and downstream side effects. If there is any difference, it is treated as a defect, traced back to the exact translation rule or logic path, and corrected before sign-off. The same regression suite is rerun on every iteration, so fixes do not introduce new inconsistencies elsewhere in the system. For environments without reliable historical test data, synthetic datasets are generated from the data dictionary, copybooks, and field relationships, then processed through both systems to validate behavior across different scenarios. By the final handover, all critical business logic has been tested end-to-end. The translation engine itself is continuously validated through 5,000+ automated translator tests covering COBOL dialect differences, embedded SQL, batch processing, file handling, numeric precision, and edge-case logic paths. AI-generated test scenarios further expand regression coverage by identifying additional execution paths and business conditions from the source code automatically.

No. The DMS toolkit can be deployed directly on your infrastructure, allowing the entire COBOL-to-Java translation process to run within your own network on standard Linux or Windows servers. Source code, datasets, generated Java output, regression results, and documentation remain fully inside your environment, which is critical for regulated and security-sensitive organizations. The on-premise deployment includes the translator, dialect-specific rewrite rules, regression testing framework, and documentation generator. Your security team can audit the full toolchain, while your engineers can run migrations and future translation cycles internally after training, without repeatedly transferring code to an external vendor.

This is where the GenAI layer earns its place. Before code translation begins, we run a business rule extraction pass that produces human-readable documentation of what each program does: calculations, decision points, data dependencies, and the conditions under which different branches execute. Your team validates the documentation, corrects anything that doesn't match institutional knowledge, and signs off. That validated rule set becomes the test oracle for the translation. Bottom line: when the Java output matches the documented behavior across regression, you have proof your business logic survived intact, with paperwork your auditors will accept.

They're the most important people on the project, both during and after. During migration, they're the source of business knowledge nobody else has. They review the extracted rules, validate the test data, and catch edge cases the toolkit can't infer from code alone. After cutover, they typically transition to maintaining the new Java system. The output is structured Java that follows modern conventions, so the learning curve is mostly Java syntax and Spring patterns rather than re-architecting from scratch. Most clients keep the COBOL team intact through the transition and find the deep system knowledge is what makes the post-migration phase smooth. The team also receives code training from our experts, for sustained system continuity.

Yes, and we usually do on systems above 2 million lines. Strangler-pattern migration works well: pick a bounded subsystem, migrate it, run it side-by-side with the legacy COBOL via APIs, then move the next subsystem. Each phase pays off on its own. Common starting points: customer-facing modules where modern UI and integration matter most, or high-cost batch processing that drives the bulk of MIPS spend. We help you pick the wedge that delivers the highest ROI in the first 6 months, then keep going from there.

Spring Boot is our default, including Spring Data for persistence and Spring Batch for job orchestration. We've also delivered Quarkus targets for clients running Kubernetes-heavy stacks, and Java EE targets where the customer environment required them. The framework choice is a configuration of the translator, not a separate engagement, so switching targets after the assessment phase is possible without restarting the project. Where your team has strong opinions about coding standards, package structure, or dependency injection patterns, we tune the code generator to match before Phase 3 starts. The output should look like code your team would have written.

As a single coordinated workstream. The same codebase analysis that drives the code translation also drives the schema design for the relational target. We generate the migration scripts (VSAM unload, normalization, load) as part of the deliverable. Source databases we've migrated from: VSAM, IMS, DB2, Adabas, DataCom, Model 204. Target databases: Oracle, SQL Server, PostgreSQL, Aurora. Cutover happens only after both code and data have passed regression on real production volumes.

AWS Transform is an agentic AI. It's good at scale, fast on simple code, and integrates well with AWS infrastructure. It's also probabilistic at the core, which means functional equivalence is validated by testing rather than guaranteed by transformation rules. For systems where AI errors are recoverable, that's a fine choice and we'd recommend it for some workloads. For financial, defense, or regulated systems where errors aren't an option, deterministic translation has a different risk profile. We have customers who use AWS Transform for parts of their portfolio and us for the parts that can't tolerate uncertainty.

Around 250,000 lines of code is our typical floor. Below that, the per-line economics get hard because the configuration work in Phase 2 is similar regardless of codebase size. For smaller projects: we sometimes recommend a tooling-led engagement where we configure the translator and your team runs it under our advisory, at a much lower price point. If your project is under 100,000 lines, we'd usually point you to one of the AI-led tools instead. The economics there favor probabilistic translation.

No. Once the modernization project is completed, the customer owns the translated application code and deliverables outright. There are no recurring runtime royalties, platform lock-ins, or mandatory ongoing license fees tied to the generated Java application. Clients receive full rights to the modernized codebase, along with the supporting documentation, regression assets, and deployment artifacts created during the engagement. Future maintenance, enhancements, and hosting can be managed internally or by any partner of your choice.