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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Mainframe developers can use AI for bounded work—such as explaining unfamiliar COBOL or JCL, drafting a routine from a clear specification, or suggesting optimization ideas—but they should remain responsible for design, security review, and validation. Available tools describe mainframe-specific capabilities, yet the evidence reviewed does not establish a universal productivity gain or a causal productivity effect for mainframe developers.
What AI can do in mainframe development
Capabilities depend on the product and version. IBM said watsonx Code Assistant for Z version 2.6 would be available to clients beginning 27 June 2025. In its announcement, IBM described natural-language COBOL code generation through a VS Code chat interface, context-aware inline suggestions, and code explanation for COBOL, JCL, PL/I, and REXX. Assembler explanation was described as entering preview. These are IBM product statements; check current official documentation for availability and supported features before making a purchase or changing a workflow. IBM’s version 2.6 announcement
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These capabilities suggest practical starting points: ask for an explanation of an unfamiliar segment, request a draft routine from a precise specification, or seek optimization ideas to investigate. Treat each response as a proposal. The tool does not know every local convention, operational dependency, or business rule unless that context is available and correctly interpreted.
What the productivity evidence does—and does not—show
An IBM Research study published 26 April 2025 examined IBM’s internal watsonx Code Assistant deployment. It included surveys of two user cohorts (669 participants in total) and unmoderated usability testing with 15 participants. The researchers report perceived productivity impact and describe potential net productivity increases, while noting that not all users experience those benefits. The sample sizes describe the study; they are not a promised gain for an individual team. The study concerns one company’s experience, not a controlled, mainframe-only estimate of AI’s causal effect on developer productivity. IBM Research’s study
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IBM also reported more than 250 completed or ongoing generative-AI projects with clients in its 2025 z17 overview. That is an IBM-reported project count, not a measurement of coding productivity or proof that a particular mainframe team will benefit. IBM’s z17 overview
Use AI for drafts and explanations, not approval
Understanding unfamiliar code
Ask for a plain-language explanation of a selected COBOL or JCL section, then verify the explanation against the actual code, surrounding modules, and established documentation. A fluent summary is not proof that the assistant recognized every branch, data assumption, or side effect.
Generating a routine
Provide a clear specification, including inputs, outputs, edge cases, relevant data definitions, and applicable local conventions. Review the generated draft line by line, compare it with the specification, and test it in the appropriate environment before use. Do not treat successful generation—or syntactically plausible output—as evidence that requirements have been met.
Exploring optimization
Use suggestions to identify alternatives worth evaluating, not as a substitute for performance analysis. Check that a proposed change preserves behavior and meets the system’s operational and performance requirements; measure it under conditions representative of the application.
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Modernization requires more than translating syntax
Legacy application behavior is shaped by dependencies and business logic as well as source syntax. IBM describes its Z modernization approach as supporting selective, incremental COBOL-to-Java transformation, with application discovery, dependency analysis, refactoring support, and automated testing in its described workflow. An earlier IBM launch announcement describes a lifecycle of discovery, refactoring, transformation, and validation. Those are descriptions of IBM’s product approach, not independent proof that a transformation is correct or will save a particular amount of time. IBM’s modernization overview IBM’s launch announcement
Application discovery and dependency analysis matter because a change that appears local may affect other components or business processes. Plan for selective changes, explicit checks of behavior, and validation across relevant dependencies rather than assuming that translating code alone completes modernization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to validate AI-generated mainframe code
- Check the specification: confirm that the output addresses the stated requirements, inputs, outputs, and edge cases.
- Review local and platform fit: inspect language use, interfaces, dependencies, and compliance with the team’s conventions and technology stack.
- Assess security: look for insecure patterns, unintended exposure of sensitive information, and assumptions that conflict with your organization’s policies.
- Run appropriate tests: use the unit, integration, regression, and operational checks relevant to the change. For modernization work, include affected dependencies and behavior, not just whether the transformed code builds.
- Keep a developer accountable: require qualified review and approval before generated code is merged, deployed, or used as a basis for further transformation.
IBM’s general developer guidance flags inaccurate output, overreliance, security vulnerabilities, and the need for more comprehensive code review as risks. It advises teams to check compatibility with their technology stack and workflow and to establish responsible-use policies and security guardrails. IBM’s AI code-generation guidance
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Evaluate a tool against your team’s work
Before adopting an assistant, compare the capabilities that matter to your environment. Do not infer deployment, data handling, language coverage, or commercial availability from a feature announcement alone; confirm current terms and documentation with the provider.
- Language and task coverage: verify support for the specific languages and work you need, such as explanation, code generation, or modernization.
- Workflow fit: check IDE and development-process compatibility, including how developers review and track generated suggestions.
- Deployment and data controls: establish where prompts and code are processed, what data is retained, and whether the arrangement meets organizational requirements.
- Explanation and traceability: decide whether developers can inspect the basis and context of suggestions well enough to review them.
- Testing support: confirm what test-generation or validation support is available, and retain independent checks appropriate to the system.
- Governance: define permitted uses, review responsibilities, security controls, and how exceptions are handled.
- Evidence of outcomes: measure results on representative team tasks rather than assuming vendor claims or broad studies predict your own experience.
IBM’s z17 overview describes AI-supported development and IBM Test Accelerator for Z, including planned natural-language test generation and AI in unit and integration testing. These are IBM descriptions of product capabilities and plans; evaluate current status and suitability for your environment in the official documentation. IBM’s z17 overview
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