Keep a legacy-system knowledge graph accurate by treating every code change as a traceable evidence update, not as a reason to rebuild everything blindly. Extract dependencies from the changed code and related artifacts, revise the affected entities and relationships, inspect downstream impact, validate the proposed graph delta with engineers, and run regression checks. Store each graph revision against the repository state that produced it.
This change-driven loop is more practical than promising a universal refresh interval or an automated accuracy score. Published work describes incremental architecture recovery, interactive analyst review, and graph-quality controls, but no source establishes one staleness limit or production threshold that fits every COBOL, PL/I, assembler, or mixed-language estate.
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Define what “accurate” means for your graph
Accuracy is not one number. A graph can contain every module but still have wrong call edges; it can have correct relationships but lag several releases; or it can be fresh yet too vague to answer a maintenance question. Write the target down before changing the pipeline.
| Accuracy dimension | Question to answer | Useful evidence |
|---|---|---|
| Entity coverage | Are the relevant programs, jobs, transactions, modules, tables, schemas and interfaces represented? | Source files, build manifests, data dictionaries, deployment descriptors and runtime inventories |
| Relation correctness | Does each calls, reads, writes, sends, schedules or inherits edge match the current artifacts? | Parser output, resolved symbols, SQL, copybooks, configuration and reviewed domain rules |
| Freshness | How soon after a commit or deployment must affected facts be updated? | Repository commit IDs, build IDs and graph revision timestamps |
| Decision usefulness | Can a maintainer answer a defined impact or modernization question with acceptable effort? | Completed change-impact investigations and test-selection records |
Set separate acceptance rules for these dimensions. For example, your release policy might require all changed public interfaces to be reviewed before deployment, while allowing lower-risk documentation edges to update overnight. Such numbers are local policy, not a published industry standard.
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Model the system with an explicit, adaptable ontology
Start by listing the entities and relationship types the maintenance team actually uses. A legacy batch estate may need programs, paragraphs, copybooks, jobs, datasets, database tables, stored procedures, transactions, message queues and external files. A distributed service may instead need repositories, packages, APIs, topics, schemas, services and deployment units.
IBM Research describes a customizable ontology and a knowledge graph that combines source code with artifacts such as data schemas for modernization analysis. Its analysts repeatedly refine entities and groupings, then examine how a change in one part affects others. See IBM’s incremental legacy-application analysis paper for the described workflow.
- Define naming rules for entities, including how generated files, aliases and copybook inclusions are represented.
- Distinguish relation semantics: a static call is not the same as a dynamic invocation, and a table read is not the same as a table update.
- Record language and platform metadata so identical names in different runtimes do not collapse into one node.
- Allow the ontology to evolve, but version schema changes and provide migration rules for existing facts.
Do not hide uncertainty in the ontology. Give candidate or inferred edges a status such as proposed, validated, rejected or superseded, with the evidence and reviewer attached.
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A graph revision is useful only when you can state which system state it represents. Build the baseline from a specific repository commit, build configuration and artifact set. Capture hashes or immutable identifiers for source archives, schemas, interface definitions, generated code and configuration exports.
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- Inventory inputs. Enumerate repositories, branches, build files, copybooks, schema registries, job-control libraries, configuration stores and deployment manifests that affect the target system.
- Normalize identities. Resolve aliases, renamed files and generated artifacts to stable entity identifiers while retaining the original path and language.
- Extract facts. Parse declarations and references, resolve symbols where possible, and ingest non-code relationships such as table ownership or job scheduling.
- Attach provenance. Store the source artifact, line or structural range, extractor version, timestamp and confidence for every edge.
- Publish a baseline revision. Record the repository commit, build inputs and ontology version in the graph metadata before analysts use the graph for impact decisions.
If an artifact cannot be obtained, mark the resulting relationship as unknown rather than silently treating the missing evidence as no dependency. This prevents a partial baseline from looking complete.
Use code changes to target re-analysis
For each commit, begin with the changed files and changed blocks, then determine which graph facts could be affected. Wang, Kong, Wang and Li describe an incremental software-architecture recovery technique that analyzes changed code, extracts dependencies and maps code-level changes to architecture-level updates. Their evaluation covered 10 open-source projects and reports better effectiveness and efficiency than the compared techniques; that result is bounded to their study setting.
- Classify the change. Separate additions, deletions, renames, signature changes, control-flow edits, data-access edits, configuration changes and generated-code updates.
- Map changed ranges to entities. Resolve each changed block to a program, method, paragraph, query, schema object or configuration key in the ontology.
- Re-extract local dependencies. Re-run the relevant language and artifact analyzers for the changed entities and their immediate context. Include callers, callees, inherited members, included copybooks and referenced tables where resolution requires them.
- Compare facts. Produce additions, removals, modified attributes and unresolved references rather than replacing the old subgraph without explanation.
- Expand the review boundary. Add architecture nodes whose relationships may have changed even when their files were untouched, such as a service contract consumed by many clients.
Incremental processing should be conservative around uncertain changes. A one-line name edit can alter dynamic dispatch or a data mapping; if the extractor cannot prove the boundary, widen the re-analysis set and flag it for review.
Propagate impact through the dependency graph
After producing a candidate delta, traverse both directions from each changed boundary. Upstream traversal finds callers, schedulers, consumers and data producers. Downstream traversal finds invoked routines, tables, files, messages and deployment components. Keep edge types and, where available, dependency strength so analysts can distinguish a direct compile-time call from a weak naming or co-change association.
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Use focused views rather than an unreadable whole-graph display. IBM’s workflow uses interactive views around logical boundaries so an analyst can refine which entities matter to a modernization question. The IEEE taxonomy paper frames impact analysis and regression testing as necessary when software changes; its discussion covers change types at different levels of granularity. Read it at IEEE Xplore.
For systems where behavior changes over time, preserve temporal information: the edge’s validity interval, the release in which it appeared and the event that removed it. A temporal view can prevent a retired interface from being treated as a current dependency while still preserving historical explanations.
- Review direct edges first; they are usually the strongest evidence for immediate breakage.
- Then inspect transitive paths until the path reaches a documented operational boundary, such as an external partner or manually verified dataset.
- Separate compile-time, deploy-time and runtime impacts. A graph path alone does not prove that a runtime branch executes.
- Export the impacted-entity set to the test-selection process, preserving the path that caused each item to be included.
Validate every proposed graph delta
Validation should compare the proposed facts with the same evidence used to create them and expose conflicts for a person to resolve. The 2026 conference abstract on LLM-assisted software-engineering knowledge graphs names graph quality assurance, semantic deduplication, incremental adaptation and explicit revision version control as parts of its framework. It reports a 12.3–18.7% relation-extraction F1 improvement and a 40–60% end-to-end training-cost reduction against strong baselines on CodeSearchNet, Stack Overflow and GitHub Issues. Those are results for that framework and corpora, not a guarantee for a legacy production graph. The abstract is available from IEEE Xplore.
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- Reject edges whose endpoint identifiers no longer exist in the baseline or current artifact set.
- Detect duplicate nodes created by path changes, case differences, generated names or aliases.
- Check relation direction and type against parser evidence; a read edge must not be emitted as a write edge.
- Flag cycles, orphaned public interfaces, unresolved symbols and schema columns referenced by no current definition.
- Compare counts and high-risk edge changes with prior revisions to catch extractor regressions.
Evidence and semantic checks
- Require a source location or artifact reference for every new high-impact edge.
- Compare similar names and descriptions to identify semantic duplicates, but send merges to review when business meaning is uncertain.
- Use contract, schema and configuration checks to catch relationships that source parsing alone cannot see.
Run regression checks based on the impact set
Impact analysis narrows the likely blast radius; it does not replace tests. Build a test plan from the changed entities, traversed dependencies and operational criticality.
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- Collect unit, component, integration, batch-control and data-reconciliation tests associated with directly changed nodes.
- Add tests for high-risk dependents, even when their source files were untouched.
- Include schema-compatibility, message-contract and deployment checks when the delta crosses an artifact boundary.
- Run broader smoke or end-to-end suites for unresolved, dynamic or externally managed dependencies.
- Store test results with the graph revision, including skipped tests and the reason for each exclusion.
A passing test suite does not prove that every graph edge is correct, and a graph warning does not automatically mean production behavior is broken. Keep the two evidence streams linked but distinct.
Keep human review in the loop where meaning is ambiguous
Legacy code often expresses business rules through naming conventions, copybooks, shared files, dynamic calls or undocumented operator practice. An extractor can identify a structural reference without knowing whether it is a critical settlement path or dead compatibility code.
Route proposed changes to the person who can validate the meaning: a system architect, domain expert, database owner or operations specialist. IBM’s described workflow includes an SME or system architect and interactive boundary refinement. Ask reviewers to confirm the edge, reject it with a reason, or mark it as conditional with the missing evidence specified.
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- Prioritize review by changed public interfaces, financial or safety-critical data, high fan-out nodes and unresolved dynamic behavior.
- Show before-and-after graph views, source locations, artifact evidence and impacted paths in one review record.
- Require two-person approval for ontology migrations or deletion of widely used relationships.
- Feed accepted corrections back into parser rules, alias maps and domain dictionaries so the same ambiguity is less likely to recur.
Choose a refresh strategy that matches the change
| Strategy | Best use | Advantages | Risks and controls |
|---|---|---|---|
| Full rebuild | Ontology migration, extractor replacement, major platform change or an untrusted baseline | Consistent recomputation across the estate | Expensive and disruptive; compare old and new graphs and review large deltas |
| Change-driven incremental update | Routine commits with reliable change detection and parsers | Fast, focused and easier to review | Can miss hidden or dynamic dependencies; widen boundaries when evidence is weak |
| Hybrid update | Most mature teams: incremental updates plus scheduled reconciliation | Balances freshness with periodic correction of drift | Requires clear rules for which artifacts trigger a wider scan |
The cited publications do not prescribe how often to rebuild or reconcile. Set a cadence from deployment frequency, incident cost, parser confidence and artifact volatility. Document it as an internal service-level objective and revisit it after escaped-impact incidents.
Make graph revisions traceable and reversible
Store graph changes as reviewable revisions rather than overwriting facts in place. A useful revision record contains:
- repository commit, branch or release and build identifier;
- ontology and extractor versions;
- input artifact identifiers and hashes;
- added, removed and modified entities and edges;
- confidence, provenance and unresolved-reference counts;
- reviewers, decisions, comments and linked test runs;
- superseded revision and rollback target.
When a parser bug is discovered, you should be able to regenerate the affected subgraph, compare it with the prior revision and restore the last accepted state without losing the audit trail. Explicit graph revision version control is also identified as a component of the 2026 framework cited above.
Worked example: a changed legacy billing program
Suppose a batch billing program changes its tax calculation and replaces a read from TAX_RATE with a service call. The maintainer should not edit one “calls” edge and stop.
- Record the commit and identify the changed paragraph, SQL statement and service client configuration.
- Re-extract the program’s data and call dependencies, including copybooks and generated client code.
- Remove the old table-read edge only when the current source and schema evidence confirm it is gone; add the service-call edge with its endpoint and contract version.
- Traverse callers, downstream invoice tables, outbound files and scheduled jobs. Mark runtime branches that depend on country or customer type.
- Ask the tax-domain owner to confirm the business meaning and the database owner to confirm whether other programs still read the table.
- Run unit tests for tax rules, contract tests for the service, batch reconciliation and representative invoice comparisons.
- Publish the graph revision with the impacted paths, test results and any unresolved dynamic calls.
If the service endpoint is selected from configuration, retain that configuration dependency and label the runtime target as conditional. The graph then communicates both what is known and what still requires operational verification.
Common failure modes and fixes
- Refreshing only changed files: Expand to affected callers, schemas, generated artifacts and configuration.
- Treating missing evidence as no edge: Use an unknown or unresolved state and assign an owner to close it.
- Trusting names as identity: Combine qualified names, language, repository and runtime metadata; review aliases.
- Accepting every automated inference: Require provenance and human approval for ambiguous or high-impact relationships.
- Using one freshness target for all facts: Set risk-based policies for interfaces, data lineage, documentation and historical edges.
- Running tests without recording graph context: Link test selection and results to the exact graph revision and commit.
- Assuming published metrics transfer directly: Treat study results as bounded evidence and measure your own estate.
What published evidence does—and does not—establish
| Source and scope | Reported contribution | What not to infer |
|---|---|---|
| IBM Research, 2022; two client applications | Customizable ontology, mixed code-and-artifact analysis and interactive impact views | Perfect automation or proof that the workflow fits every legacy platform |
| Wang et al., 2022; 10 open-source projects | Incremental architecture recovery driven by changed code, dependency analysis and mapping to architecture updates | A universal accuracy or speed advantage on your codebase |
| IEEE taxonomy, 2012 | Change-impact analysis and regression testing as software-evolution practices across change granularities | A prescribed graph schema, cadence or test threshold |
| 2026 IEEE conference abstract; three software-engineering corpora | Framework components for quality assurance, semantic deduplication, incremental adaptation and revision control, with the reported F1 and training-cost comparisons | Those model metrics as graph-freshness or production-quality guarantees |
The practical conclusion is disciplined rather than mysterious: define the facts your graph must support, update the smallest defensible region after each change, widen analysis when dependencies are uncertain, validate with people and artifacts, test the impacted behavior, and preserve every decision in a versioned history.
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