Use generative AI to analyse and summarise undocumented legacy code and verify it against real test inputs.
Digital Transformation & Artificial Intelligence
Using Generative AI to Analyse, Document and Migrate Legacy Code
Shows engineering teams how to use generative AI responsibly to analyse undocumented legacy systems, generate documentation and support incremental migration.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Every long-running organisation keeps at least one system that nobody currently on staff fully understands, kept alive because rewriting it looks riskier than leaving it alone. Generative AI changes that calculation by making it practical to analyse and summarise undocumented modules, reconstruct buried business rules and produce a first-pass translation into a modern language, provided the output is verified rather than trusted outright. This course works through that verification discipline: cross-checking AI explanations of legacy logic against real test inputs, recovering business rules through a mix of code analysis and conversations with long-serving staff, and generating documentation and dependency maps that the whole migration team can rely on. Migration itself is planned around the strangler-fig pattern, moving one component at a time behind a stable interface so rollback is always available. Later sessions cover regression testing for functional equivalence and reviewing AI-generated code for security flaws and licensing risk before it reaches production, so speed never comes at the cost of a system nobody can be held accountable for.
Expected Learning Outcomes
Recover business rules embedded in legacy logic and confirm they still reflect current business intent.
Generate technical documentation and dependency maps that a migration team can rely on and maintain.
Produce and review an AI-assisted translation of legacy code into a target language or platform.
Apply the strangler-fig pattern to migrate components incrementally behind a stable interface.
Build a regression test suite that proves functional equivalence between legacy and migrated systems.
Review AI-generated code for security weaknesses and licensing risk before it reaches production.
Who Should Attend
Software engineers assigned to modernise a legacy system with limited original documentation.
Technical leads planning a migration strategy for a business-critical legacy application.
Enterprise architects mapping dependencies before scheduling legacy components for retirement.
Quality assurance staff responsible for proving equivalence between old and migrated systems.
Engineering managers weighing AI-assisted migration against a full manual rewrite.
Security reviewers who must assess AI-generated code before it is accepted into production.
Course Modules
Select any module to see its sessions and points.
01Understanding Legacy Systems Before Migration Begins
2 sessions · 8 points
Session 1Using Generative AI to Comprehend Undocumented Legacy Code
- Prompt a language model to summarise a legacy module's purpose from its source code and inline comments.
- Cross-check an AI-generated explanation of legacy logic against actual output on a known set of test inputs.
- Identify sections of legacy code the model cannot explain with confidence and flag them for manual review.
- Combine static analysis output with generative AI summaries so structural facts anchor the AI's narrative.
Session 2Reconstructing Business Rules Buried in Legacy Logic
- Extract embedded business rules from conditional logic that was never documented outside the code itself.
- Interview long-serving staff to confirm whether an extracted rule still reflects current business intent.
- Record each recovered business rule in a shared repository the migration team can trace back to its source line.
- Distinguish a deliberate business rule from a historical workaround the new system should not reproduce.
02Generating Documentation and Dependency Maps
2 sessions · 8 points
Session 1Producing Technical Documentation With AI Assistance
- Generate first-draft technical documentation from source code and then edit it for accuracy with the original team.
- Produce function-level summaries that state inputs, outputs and side effects for every legacy component.
- Version generated documentation alongside the code so both stay synchronised as the migration proceeds.
- Flag AI-generated documentation claims that cannot be verified against the code and mark them as assumptions.
Session 2Mapping Dependencies Across the Legacy Estate
- Map data flows and integration points between the legacy system and every upstream and downstream application.
- Identify undocumented interfaces that other systems depend on before any component is scheduled for retirement.
- Use dependency analysis to sequence migration so a component is not moved before its dependents are ready.
- Record every discovered integration in a shared inventory the migration and operations teams both maintain.
03Planning and Executing AI-Assisted Migration
2 sessions · 8 points
Session 1Translating Legacy Code Into a Target Language or Platform
- Use a language model to produce a first-pass translation of legacy code into the target language.
- Review AI-translated code line by line against the original for logic that was simplified or dropped.
- Preserve legacy edge-case handling explicitly, since a model may translate the common path and miss the exception.
- Benchmark translated code for performance against the legacy version before accepting the conversion as complete.
Session 2Applying the Strangler-Fig Pattern to Migrate Incrementally
- Wrap a legacy component behind an interface that lets new and old implementations run side by side.
- Route a small, low-risk share of traffic to the new implementation before increasing it incrementally.
- Retire each legacy component only once its replacement has matched output over a defined observation period.
- Keep a documented rollback path to the legacy component throughout each stage of the migration.
04Validating, Securing and Governing the Migration
2 sessions · 8 points
Session 1Testing for Functional Equivalence Between Old and New Systems
- Build a regression test suite from legacy system outputs to confirm the new system behaves equivalently.
- Run old and new systems in parallel on live data and compare outputs before switching over fully.
- Generate additional test cases with AI assistance to cover logic branches the original test suite never reached.
- Sign off functional equivalence against documented business rules, not only against passing test cases.
Session 2Reviewing AI-Generated Code for Security and Licensing Risk
- Review AI-generated code for insecure patterns before it is merged into the migrated codebase.
- Check AI-suggested code and generated tests for licensing terms that could conflict with the organisation's own.
- Require a qualified engineer to approve every AI-generated code change before it reaches production.
- Log which parts of the migrated system were AI-assisted so future audits know where to focus review.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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