As experienced COBOL engineers retire at an accelerating pace, organizations face a critical challenge: understanding decades-old mainframe applications that process trillions of dollars in transactions. Google’s Gemini AI promises to automate this understanding, but can a generative AI model reliably extract complex business logic from 40-year-old COBOL code?
Understanding COBOL applications with Gemini represents an emerging approach to legacy modernization, combining Google’s advanced language models with mainframe-specific analysis tools. While Gemini offers capabilities that can accelerate certain aspects of code comprehension, significant limitations remain – particularly around accuracy, context handling, and enterprise reliability requirements.
This guide explains how Gemini works for COBOL analysis, what it can and cannot do, and which approach delivers trustworthy results for critical modernization decisions.
What is Gemini for COBOL Application Understanding?
Gemini for COBOL application understanding refers to using Google’s Gemini AI models – particularly through tools like the Mainframe Assessment Tool and Gemini Code Assist – to analyze, document, and extract business logic from legacy COBOL codebases. While COBOL is not officially supported in Gemini Code Assist’s verified languages, the tool is being used in mainframe modernization to generate code explanations, specifications, and migration assistance.
Google Cloud’s approach combines Gemini’s large language models with mainframe-specific context to help organizations make sense of legacy applications. The goal: accelerate modernization timelines by automating the understanding phase that typically requires senior engineers to manually explain code line-by-line.
Organizations are turning to AI-powered tools like Gemini because the COBOL workforce is shrinking rapidly while the systems themselves remain mission-critical. COBOL still powers 80% of in-person credit card transactions and 95% of all ATM transactions globally. When the engineers who built these systems retire, institutional knowledge disappears unless captured automatically – making understanding COBOL applications with Gemini and similar AI tools increasingly important for enterprises.
How Google’s Gemini Analyzes COBOL Code
Google offers several tools powered by Gemini models for legacy code analysis, each targeting different aspects of the modernization challenge.
Mainframe Assessment Tool (MAT) with Gemini
The Mainframe Assessment Tool is Google Cloud’s primary offering for COBOL analysis, now generally available. MAT uses Gemini models to thoroughly assess and analyze entire mainframe estates, including applications and data. This is the core technology powering understanding COBOL applications with Gemini at an enterprise scale.
Key capabilities include:
Code Analysis: MAT supports analysis of COBOL programs, copybooks, JCL, PL/I, and related mainframe languages. The tool performs in-depth code analysis, generating clear code explanations and summarized application logic.
Dependency Mapping: The tool identifies application dependencies, showing how DB2 tables are used in JCL batch jobs and COBOL programs. The Databases Lineage tab displays the called program, accessed table, and corresponding usage. The CICS Calls tab shows CICS calls made from each program and their parameters.
Automated Documentation: MAT generates paragraph-level summaries for COBOL code through detailed summary generation. The Module Types tab provides information about COBOL and JCL structure, and the assessment results include details like lines of code and number of calls.
Test Case Generation: The platform can generate initial test cases for assessment specifications, helping teams verify modernization accuracy.
Recent enhancements (version 1.4.0) include significant specification improvements for COBOL and JCL, while version 1.3.4 added parsing enhancements and improved program-level specifications for COBOL, Easytrieve, and Assembler.
Gemini Code Assist for Legacy Modernization
Gemini Code Assist is positioned as Google’s AI coding assistant, though its COBOL support comes with important caveats. The tool was officially verified for 22 programming languages – COBOL is not included in this list.
However, according to Google’s documentation, “the Gemini large language models that are used by Gemini for Google Cloud are trained on a vast set of coding examples within the public domain, and therefore LLMs are often able to understand and provide assistance on a wide variety of coding languages.”
When used for mainframe modernization, understanding COBOL applications with Gemini Code Assist involves:
Code Translation: Gemini can suggest code translations from COBOL to modern languages like Java, C#, and Python. The Mainframe Code Rewrite extension for Visual Studio Code integrates mainframe-specific generative AI code analysis capabilities.
Specification Generation: The tool generates application specifications, code summaries, and explanations when provided with COBOL programs.
IDE Integration: Cloud Code and Gemini Code Assist plugins work within VS Code and supported JetBrains IDEs, though mainframe-specific features are primarily in the Mainframe Code Rewrite extension.
What Gemini Can Generate for COBOL
When understanding COBOL applications with Gemini, the AI-powered tools can produce:
- High-level summaries of program purpose and functionality
- Code explanations translating technical COBOL into more readable descriptions
- Flow documentation showing how programs execute
- Suggestions for code translation to modern languages
- Initial test case frameworks based on code analysis
The output quality depends heavily on the code complexity, available context, and whether the analysis is performed through specialized mainframe tools (MAT) versus general-purpose Gemini Code Assist.
COBOL-Specific Challenges for AI Understanding
COBOL presents unique obstacles for AI-powered code analysis that don’t exist with modern languages. Understanding these challenges is critical for setting realistic expectations about what Gemini – or any LLM – can reliably deliver.
Limited Training Data for COBOL
Unlike modern languages that benefit from extensive online documentation, open-source repositories, and active developer communities, COBOL exists largely in proprietary archives and decades-old manuals. This creates a fundamental challenge for LLM training.
As one industry expert noted, “Try finding a COBOL engineer. It’s next to impossible,” highlighting the scarcity of COBOL expertise. But the training data gap is equally severe. Most COBOL code is proprietary, locked behind enterprise firewalls, and never published to public repositories where LLMs could learn from it.
This limited exposure means AI struggles significantly when dealing with COBOL. The lack of readily available training data makes it difficult for AI to generate, refactor, or document COBOL code effectively. While Gemini excels at explaining Python or JavaScript – languages with millions of public examples – it operates at a disadvantage with COBOL.
Context Window Limitations
Large COBOL applications can span millions of lines of code across thousands of programs. Even Gemini’s generous token context window (up to 128,000 input tokens in chat) struggles with the sheer scale of enterprise mainframe systems when understanding COBOL applications with Gemini.
The practical impact: Microsoft’s team working on COBOL migration using AI agents noted that “limited token windows led to loss of relevant context.” They found that “one of the hardest challenges was managing the call-chain structure – understanding which module calls which, and at what depth,” with teams managing to reach level 3 but not beyond.
This means Gemini can analyze code snippets but misses cross-program dependencies and complex call hierarchies that define how business logic actually executes across a mainframe application. The LLM sees trees but misses the forest.
Complex Business Logic Patterns
COBOL applications have accumulated 40+ years of business rules, patches, and modifications. The code contains:
- Nested conditionals spanning hundreds of lines
- GOTO statements creating non-linear flow
- Cryptic eight-character variable names (WSACCT01, TOTLAMT, ERRFLG)
- Implicit logic dependent on decades of organizational conventions
- Business rules scattered across multiple programs with no clear documentation
AI may efficiently translate COBOL to modern languages like Java, yet it struggles to grasp the deeper business intents behind these codes. This can result in code that is technically correct but functionally inadequate, potentially leading to mission-critical failures.
Hallucination Risks on Critical Business Rules
The most dangerous limitation when understanding COBOL applications with Gemini: AI’s probabilistic nature vs. the deterministic requirements of business logic.
Research on LLM code understanding shows that “generative AI systems operate on probabilistic inference, meaning the same input can yield different outputs – an unacceptable risk for mission-critical enterprise systems.” When using agentic AI approaches for COBOL analysis, additional failure modes emerge that can compound these hallucination risks.
When analyzing COBOL programs, Gemini might generate plausible-sounding explanations of business rules that are factually incorrect. The LLM completes patterns from its training data rather than verifying against actual code execution paths.
For example, when asked to explain a payment validation routine, Gemini might describe validation steps that sound reasonable but miss the critical edge case handling buried in a nested IF statement five levels deep. The business analyst reading the AI’s explanation has no way to verify its accuracy without manually checking the source code – defeating the purpose of automation.
Gemini’s Limitations for COBOL Application Understanding
Understanding Gemini’s specific weaknesses helps organizations make informed decisions about when to use AI assistance for understanding COBOL applications with Gemini and when to require more rigorous approaches.
Not Officially Verified for COBOL
Gemini Code Assist’s documentation explicitly lists 22 verified programming languages: Bash, C, C++, C#, Dart, Go, GoogleSQL, Java, JavaScript, Kotlin, Lua, MatLab, PHP, Python, R, Ruby, Rust, Scala, SQL, Swift, TypeScript, and YAML. COBOL is absent from this list.
What does “not officially verified” mean? Google hasn’t tested and validated Gemini’s COBOL capabilities to the same standard as supported languages. There are no published accuracy metrics, no dedicated COBOL training examples, and no guarantees about output quality.
Organizations using Gemini for COBOL analysis are essentially beta testing the tool on their production codebases. The LLM may work reasonably well in some cases and fail unpredictably in others.
Snippet-Level vs. Application-Wide Understanding
When understanding COBOL applications with Gemini, the tool analyzes code within its context window – typically individual programs or functions. This snippet-level approach fundamentally misses how mainframe applications work.
Business processes in COBOL systems flow across dozens of programs:
- A CICS screen program captures user input
- Validates data by calling a copybook validation routine
- Passes control to a business logic program
- Which calls database access programs
- Which trigger batch jobs for downstream processing
- Which update multiple interconnected tables
Gemini sees individual pieces but cannot trace the complete flow from user action to business outcome. Static analysis tools can follow these execution paths deterministically – LLMs cannot.
The result: incomplete understanding that misses critical dependencies, creating risk for modernization projects where “everything looked fine until testing revealed a missing validation that caused production issues.”
Lack of Traceability and Verification
When Gemini explains a COBOL program, how do you verify the explanation is correct?
Traditional static analysis tools provide traceability: “This business rule exists because line 347 contains IF ACCT-BALANCE > OVERDRAFT-LIMIT.” Every insight links directly to source code.
AI-generated explanations lack this grounding. The LLM synthesizes an explanation based on pattern matching, but you cannot trace the reasoning back to specific code elements. This creates “black box” concerns for enterprise governance.
The main transparency concern is the “black-box” nature, which limits transparency and makes tools difficult to govern in enterprise environments that demand predictability and auditability. When AI solutions generate code or suggest modifications, it is essential for engineers to understand the rationale behind these changes to verify their correctness.
For compliance-heavy industries (banking, insurance, healthcare), this lack of traceability is often a dealbreaker. Auditors require verifiable evidence that business rules were correctly extracted and documented – AI explanations without code references don’t meet this standard.
Translation Focus vs. Understanding Focus
When understanding COBOL applications with Gemini, it’s important to recognize that Google’s tools are optimized for code conversion – translating COBOL to Java or C# for migration projects. This is valuable for certain modernization approaches, but it’s not the same as extracting and documenting business understanding.
Organizations embarking on modernization need to answer questions like:
- What business rules govern customer eligibility?
- How do we handle transaction reversals?
- What validation rules apply to different account types?
- Where are calculation formulas for interest, fees, and penalties?
Gemini’s translation capabilities don’t directly address these questions. The tool can convert COBOL syntax to Java syntax, but business analysts still can’t easily understand what the code does in business terms.
This gap between translation and understanding is why many modernization programs stall. Converting code is the easy part – understanding what to build in the new system requires extracting business logic in human-readable form.
Best Practices for Using AI to Understand COBOL Applications
When understanding COBOL applications with Gemini and similar AI tools, it’s crucial to know the capabilities and limitations. When does AI assistance make sense, and when do you need more rigorous approaches?
When Generative AI Like Gemini Can Help
Understanding COBOL applications with Gemini and similar LLMs can provide value in specific scenarios:
Quick Code Snippet Explanations: For understanding isolated COBOL paragraphs or procedures, Gemini can generate helpful high-level summaries. If you’re reviewing a 50-line calculation routine and need a quick overview of what it does, AI explanation can save time.
Initial High-Level Overviews: When first encountering a COBOL program, Gemini can provide a general summary of purpose and structure. This gives developers a starting point before diving into detailed analysis.
Terminology Translation: AI excels at translating cryptic variable names and technical jargon into more readable descriptions. WS-ACCT-BAL-TOT becomes “workspace account balance total.”
Generating Baseline Documentation Drafts: Gemini can create first-draft documentation that human experts then review, edit, and validate. This accelerates the documentation process while maintaining accuracy through human oversight.
Accelerating Junior Developer Learning: New team members learning COBOL can use AI explanations as a learning aid, though they should verify understanding with senior engineers.
When Deterministic Analysis is Essential
Certain modernization scenarios demand accuracy and traceability that understanding COBOL applications with Gemini alone cannot provide:
Critical Business Rule Extraction: When extracting business rules through reverse engineering legacy code that will determine new system requirements, hallucinations are unacceptable. A missed validation rule or incorrect calculation formula causes production defects and costly rework.
Complete Application Dependency Mapping: Understanding how programs, copybooks, JCL jobs, and database tables interconnect requires following execution paths deterministically across millions of lines of code.
Production Cutover Decisions: Before switching from legacy to modern systems, teams need verified evidence that all business logic was captured and correctly implemented. Probabilistic AI outputs don’t meet this standard.
Compliance and Audit Requirements: Regulated industries require traceable documentation showing how business rules were identified and validated. Every extracted rule must link back to source code for auditor verification.
Verification and Validation Needs: Test case generation and validation require deterministic understanding of what the code actually does – not what it probably does.
Hybrid Approach: Combining Deterministic + AI
The most effective strategy combines deterministic static analysis with AI-powered explanation:
- Deterministic Extraction: Use static analysis to extract business rules, flows, and dependencies with complete accuracy. Every insight is grounded in actual code execution paths.
- AI Translation: Apply AI to translate technical findings into human-readable business language. The LLM makes deterministic results accessible to business analysts and non-technical stakeholders.
- SME Validation: Subject matter experts review AI-generated explanations, edit for accuracy, and add organizational context.
- Maintain Verification: Keep traceability from business rule descriptions back to source code lines, enabling validation and compliance.
This workflow delivers both accuracy and readability – the deterministic foundation prevents hallucinations while AI enhances human comprehension.
How to Verify AI-Generated COBOL Insights
When understanding COBOL applications with Gemini or similar tools, implement verification practices:
Cross-Reference with Source Code: Always check AI explanations against actual COBOL programs. If Gemini says “the program validates account numbers,” find the validation code and verify the logic matches the description.
SME Review and Validation: Have experienced COBOL developers review all AI-generated documentation. They can spot hallucinations and incorrect interpretations that business analysts might miss.
Test Case Verification: Generate test cases based on AI explanations and run them against the actual application. Discrepancies reveal where the AI misunderstood business logic.
Comparison with Deterministic Analysis: If available, compare AI outputs with results from static analysis tools. Differences indicate where the LLM guessed incorrectly.
Look for Hallucination Red Flags: Be suspicious of overly confident explanations, business rules that seem too simple, or logic that doesn’t account for edge cases. Real COBOL applications are complex – if the AI explanation seems too clean, it’s probably incomplete.
Deterministic + AI: A Better Approach to COBOL Understanding
The limitations of understanding COBOL applications with Gemini and other pure LLM approaches have driven development of hybrid methodologies that combine deterministic accuracy with AI-powered readability.
Why Pure LLMs Fall Short for Enterprise COBOL
Raw LLMs like Gemini, ChatGPT, and Claude – when used standalone – face fundamental limitations for enterprise legacy analysis:
Hallucination Risks: LLMs generate plausible-sounding explanations based on pattern matching. With COBOL’s limited training data and complex business logic, the risk of incorrect explanations is high. Research shows LLM code hallucination rates increase with code complexity.
Context Limitations: Even with large token windows, LLMs cannot process entire mainframe applications. They miss cross-program dependencies and holistic business process flows.
No Traceability: AI outputs cannot be verified against source code. When an LLM explains a business rule, you cannot trace the explanation back to specific code elements.
Probabilistic vs. Deterministic: Enterprise modernization requires deterministic guarantees. The same COBOL code should always produce the same business rule extraction – not probabilistic variations.
Enterprise Reliability Needs: Financial institutions, insurance companies, and government agencies cannot accept AI tools that “usually get it right.” They need verified, auditable results for mission-critical systems.
How Deterministic Analysis Grounds AI
A deterministic + AI hybrid approach addresses LLM weaknesses:
Static Analysis Provides Verified Foundation: Deterministic analysis extracts business rules, flows, and dependencies by following actual code execution paths. No guessing, no hallucinations – just facts about how the code executes.
Every Insight Traceable to Source Code: Each extracted business rule links directly to the COBOL statements that implement it. Auditors and developers can verify accuracy by examining source code.
Complete Application Coverage: Deterministic tools analyze entire applications, following flows across all programs, copybooks, and dependencies. No context window limits – the analysis continues until the complete business process is mapped.
AI Enhances Readability Without Sacrificing Accuracy: After deterministic extraction, AI translates technical findings into business language. The LLM explains “this validation routine checks account eligibility based on balance and status” instead of showing cryptic COBOL code – but the underlying facts come from static analysis, not pattern matching.
Trust + Human Readability: Organizations get both reliability (deterministic foundation) and accessibility (AI-powered explanations). Business analysts can understand the logic, while technical teams can verify every detail against source code.
Real-World Results: Deterministic + AI vs. Pure LLM
Organizations using hybrid approaches report significantly better outcomes:
90% Faster Business Rule Extraction: Compared to manual line-by-line code review, deterministic + AI tools extract comprehensive business rules in hours instead of weeks. Senior engineers are freed from explaining code while analysts get verified documentation.
Zero Hallucinations: Because insights are grounded in static analysis, there are no AI hallucinations or incorrect business rules. Every extracted rule is verifiable against source code.
Complete Application Flows: Unlike snippet-level LLM analysis, deterministic tools map entire business processes from entry points to exits across millions of lines of code. Nothing is missed.
Proven on 100M+ Lines of Code: Hybrid approaches have been validated on massive enterprise COBOL systems – the scale where pure LLMs struggle most.
Enterprise-Grade Reliability and Security: Tools combining deterministic analysis with AI can be deployed on-premise in air-gapped environments, using internal LLMs for maximum security. They meet SOC 2 and ISO 27001 compliance standards.
What This Means for Modernization Programs
The deterministic + AI approach transforms how organizations tackle legacy modernization:
De-Risked Timelines: With verified business rule extraction, modernization programs avoid the late surprises and rework that come from incomplete understanding. Teams know what they have before building what comes next.
Analysts Get Business Rules Without Senior Engineer Bottleneck: Business analysts can extract and verify business rules independently, without waiting weeks for senior engineer availability. The knowledge bottleneck is eliminated.
Traceable Documentation for Compliance: Every business rule links to source code, providing the audit trail that regulated industries require. Compliance teams can verify that modernization captured all critical logic.
Confident Modernization Decisions: When choosing between rewrite, refactor, or replace strategies, teams have comprehensive, accurate understanding to inform their decisions. No guessing about what the legacy system actually does.
Faster, More Accurate Than Pure AI or Pure Manual: Hybrid approaches deliver speed (hours vs. weeks) and accuracy (zero hallucinations) that neither pure manual analysis nor pure LLM tools can match.
Comparing Approaches: Gemini vs. Hybrid Analysis vs. Manual
| Criteria | Google Gemini | Deterministic + AI (Swimm) | Manual Analysis |
|---|---|---|---|
| Accuracy/Reliability | Medium – hallucination risk on complex logic | High – deterministic foundation prevents errors | High – but error-prone at scale |
| Speed | Fast for snippets, slow for applications | Hours for complete apps | Weeks to years |
| Coverage | Snippet/program level | Application-wide | Complete but time-consuming |
| Traceability | None – AI explanations not linked to code | Every insight links to source code | Manual traceability possible |
| Business Rule Focus | Translation-focused, not extraction-focused | Purpose-built for business rule extraction | Can extract but very slow |
| Hallucination Risk | High – probabilistic LLM | Zero – deterministic grounding | Low (human error possible) |
| Context Limits | 128K tokens – misses cross-program flows | Unlimited – analyzes complete systems | No limits but attention/time limits |
| Cost | LLM usage fees + engineer time | Tool cost + minimal engineer time | High senior engineer time |
| Best For | Code translation, quick explanations | Critical modernization, business rule extraction | Small-scale or highly specialized analysis |
| COBOL Support | Not officially verified | Specialized for COBOL/mainframe | Native understanding |
Tools for Understanding COBOL Applications in 2025
Organizations evaluating approaches for understanding COBOL applications with Gemini and alternatives have multiple options for legacy code analysis, each with different strengths and use cases. For a comprehensive overview of the COBOL tooling landscape, see our guide to the best COBOL tools in 2025.
AI only tools
Google Gemini (MAT & Code Assist): Google’s offering for understanding COBOL applications with Gemini AI for analysis and migration. Strengths include cloud-native architecture and integration with Google Cloud. Limitations include lack of official COBOL verification and hallucination risks on complex logic.
IBM Watson X Code Assistant: IBM’s LLM-based tool for mainframe modernization. Purpose-built for COBOL, PL/I, and JCL environments. Provides application discovery, refactoring capabilities, and code generation.
GitHub Copilot: General-purpose AI coding assistant with limited COBOL support. Better for modern languages than legacy systems.
CobolCopilot: Specialized COBOL AI tool focused on documentation and modernization assistance.
Deterministic Static Analysis Tools
CAST: Comprehensive application intelligence platform focused on technical debt analysis, code quality metrics, and complexity assessment. Not focused on business rule extraction.
IBM ADDI (Application Discovery and Delivery Intelligence): Mainframe-specific discovery tool for dependency analysis and impact assessment within the IBM ecosystem.
Micro Focus: Broad suite of legacy analysis and modernization tools covering multiple aspects of application lifecycle.
Hybrid Deterministic + AI Tools
Swimm: Combines deterministic static analysis with AI explanations to extract business rules, flows, and dependencies from COBOL applications. Key features include:
- Deterministic Analysis + AI Explanations: Static analysis provides accuracy and traceability while AI makes findings human-readable in business language
- Business Rule Extraction Focus: Purpose-built for extracting and documenting business logic, not just code quality metrics
- Application-Wide Flow Analysis: Maps complete business processes across entire applications without context window limitations
- Traceable Insights: Every business rule links back to source code for verification and compliance
- Enterprise-Scale Proven: Validated on 100+ million lines of legacy code across complex mainframe systems
- On-Premise Deployment: Available for air-gapped environments with internal LLM support
Organizations serious about modernization typically need hybrid tools that balance AI assistance with deterministic accuracy – particularly when business rule extraction and compliance requirements are critical. While understanding COBOL applications with Gemini can provide value in specific scenarios, enterprise-grade modernization demands the reliability of deterministic analysis.
Conclusion
Understanding COBOL applications with Gemini represents an emerging capability in the AI-powered modernization landscape. Google’s Mainframe Assessment Tool and Gemini Code Assist can accelerate certain aspects of legacy code analysis, particularly for high-level overviews and code translation tasks.
However, significant limitations remain when understanding COBOL applications with Gemini. COBOL is not officially verified by Gemini Code Assist, hallucination risks persist on complex business logic, context windows prevent complete application analysis, and lack of traceability creates governance challenges. For organizations making critical modernization decisions, these limitations often outweigh the benefits of pure LLM approaches.
Best practices for understanding COBOL applications require hybrid strategies that combine deterministic static analysis with AI-powered explanation. This approach delivers both accuracy and readability – deterministic foundations prevent hallucinations while AI makes findings accessible to business stakeholders.
Critical modernization decisions demand verified, traceable insights that can withstand audit scrutiny and production validation. When the cost of errors includes delayed timelines, budget overruns, and production defects, the reliability of deterministic analysis becomes essential – whether you’re understanding COBOL applications with Gemini or any other AI tool.
Ready to understand your COBOL applications with enterprise-grade reliability? Swimm’s application understanding platform combines deterministic analysis with AI to extract business rules in hours, not weeks – with zero hallucinations and full traceability. Request a demo to see comprehensive COBOL understanding in action.
