Introduction: The Growing Need for Software Interoperability in Engineering

Modern engineering projects rely on a complex ecosystem of software tools—from computer-aided design (CAD) and finite element analysis (FEA) to building information modeling (BIM), simulation, and product lifecycle management (PLM). Each suite excels at its specific task, but the flow of data between them often remains fragmented. Engineers spend countless hours manually transferring data, converting file formats, and reconciling mismatched values. These inefficiencies not only slow down project timelines but also introduce errors that can cascade into costly rework or even safety issues.

Refactoring—the disciplined restructuring of existing code and workflows—offers a systematic way to break down these silos. Instead of replacing entire software stacks (a high-risk, expensive undertaking), refactoring targets the interfaces, data models, and integration points that govern how tools communicate. By improving interoperability through targeted code improvements, engineering firms can achieve seamless data exchange without disrupting their current investments.

The Persistent Interoperability Challenge

Interoperability is not a “nice-to-have” feature; it is a fundamental requirement for collaborative engineering. Yet many organizations struggle with outdated integration approaches. Common pain points include:

  • Incompatible Data Formats: CAD systems often use proprietary binary files (e.g., .dwg, .sldprt) that cannot be directly read by analysis or simulation tools. Even when neutral formats like STEP or IGES are available, translation errors—such as lost parametric constraints or surface mismatches—are frequent.
  • Varying Data Standards: Different engineering disciplines follow different schemas. For example, building information models rely on IFC (Industry Foundation Classes), while mechanical models may use AP242 or JT. Bridging these standards requires careful mapping and validation.
  • Limited or Deprecated APIs: Many legacy engineering tools were never designed with modern RESTful or graphQL APIs. Their integration points may rely on outdated COM interfaces, file-based batch processing, or manually exported CSV files.
  • Technical Debt in Integration Code: Over time, bespoke scripts and middleware accumulate workarounds and patches. These “spaghetti integrations” become fragile, making any change risky and time-consuming.

The result is a workflow that feels like a patchwork of manual steps, custom translators, and duplicated data. Refactoring aims to replace these ad-hoc solutions with clean, maintainable integration layers.

Core Strategies for Refactoring Toward Interoperability

Refactoring for interoperability is not a one-size-fits-all activity. The right approach depends on the software stack, team expertise, and project requirements. However, several high-impact strategies consistently deliver results:

1. Adopt and Enforce Standard Data Formats

Using open, widely-supported data formats reduces translator fragility and future-proofs your data. Key formats in engineering include:

  • IFC (Industry Foundation Classes) for BIM and civil engineering. The buildingSMART standard is the global benchmark for exchanging building and infrastructure data.
  • STEP (ISO 10303) for exchanging product model data between CAD/CAE/PLM systems, especially AP242 for 3D-based engineering.
  • OpenAPI (formerly Swagger) for defining RESTful APIs that enable direct data exchange between web-based engineering tools.
  • JSON and XML for lightweight configuration, parameter, and metadata transfers.

When refactoring, replace proprietary intermediate file formats with these standards. For instance, if your workflow currently exports a .csv from CAD and then imports it into FEA with manual column mapping, refactor the exporter to output a structured JSON document that includes semantic definitions. This eliminates guesswork and allows automated validation.

2. Develop and Modernize APIs

A well-designed API acts as a universal translator between engineering suites. Instead of sharing files, systems can call each other’s endpoints to retrieve geometry, simulation results, or lifecycle state. When refactoring integration code:

  • Identify the most frequent data exchanges and create dedicated API endpoints for them (e.g., /cad/parts/{id}/geometry).
  • Use versioned APIs to allow gradual migration without breaking existing consumers.
  • Implement authentication, rate limiting, and logging to ensure reliability and auditability.
  • Consider GraphQL for complex queries where clients need selective data from multiple sources.

Many modern PLM and simulation platforms already expose REST APIs. If your legacy tool lacks one, consider using a wrapper or a microservice that exposes its functionality through a clean HTTP interface, then refactor the consuming applications to call that API.

3. Modularize Monolithic Integration Code

Legacy integration scripts often grow into large, tangled modules that handle many responsibilities—format conversion, transformation, error handling, and logging. Refactoring breaks these monoliths into smaller, single-purpose modules or microservices. Benefits include:

  • Each module can be updated, tested, and deployed independently.
  • Reusable modules (e.g., a STEP-to-JSON converter) can be shared across teams.
  • Fault isolation: a bug in the CAD parser does not bring down the entire integration pipeline.

Apply domain-driven design principles: create bounded contexts for each engineering domain (CAD, FEA, PLM) and build adapters that translate between their internal representations and a common data model (canonical schema).

4. Use Middleware and Integration Platforms

When direct API development is not feasible—for instance, when dealing with legacy software that cannot be easily modified—middleware can bridge the gap. Integration platforms (iPaaS) like MuleSoft, Dell Boomi, or open-source Apache Camel provide pre-built connectors, data transformation engines, and routing capabilities. During refactoring:

  • Map existing file-based or database-level integrations to the middleware’s standard connectors.
  • Implement data mapping and validation rules once in the middleware, rather than inside every consuming application.
  • Use message queuing (e.g., RabbitMQ, Apache Kafka) to decouple real-time systems from batch processes.

Middleware does not eliminate the need for clean interfaces on the application side, but it centralizes integration logic and makes future changes less disruptive.

Practical Steps to Refactor Engineering Workflows

Refactoring is an iterative, incremental process. A successful interoperability refactoring project typically follows a sequence of steps:

Step 1: Audit Existing Data Flows

Document every point where data moves between engineering software suites. Include the format, the frequency, the responsible person (or script), and known issues. Tools like process mining or simple flowcharts can visualize the current state. Identify the “hot spots” where most manual effort or errors occur.

Step 2: Identify Quick Wins with High Impact

Focus refactoring efforts on the exchanges that cause the most delays or rework. For example, if converting CAD assemblies to FEA-ready models consumes three days per week, prioritize building a clean converter that preserves geometric features and materials from the native CAD format to the simulation solver’s native format.

Step 3: Implement Incrementally with Tests

Refactoring without tests is dangerous. Before altering integration code, write automated tests that validate the data contract (e.g., “a cube with side 1m exported from CAD must appear as a 1m cube in FEA”). Then refactor in small, reversible steps, ensuring each change passes the test suite. This approach reduces risk and builds confidence.

Step 4: Retire Legacy Integration Points

Once a new, clean integration is in place and verified, systematically deprecate the old file-based or manual workflows. Communicate changes to all stakeholders and provide documentation. Use feature flags or versioning to allow a gradual cutover.

Real-World Benefits of Interoperability Refactoring

Organizations that invest in interoperability refactoring report measurable improvements:

  • Reduced Manual Data Entry: One automotive supplier eliminated 40 hours of manual geometry cleaning per week after refactoring its CAD-to-CFD integration using STEP and a custom API.
  • Faster Iterations: An architecture firm cut design validation cycles from five days to two by switching from batch file exports to a live IFC HTTPS feed between their BIM tool and structural analysis software.
  • Lower Error Rates: A civil engineering contractor reduced geometry mismatches by 90% after implementing a validation gateway that checked IFC compliance before data entered the simulation pipeline.
  • Improved Collaboration: With a common data model and APIs, mechanical and electrical teams could work on the same digital twin without “owning” separate, inconsistent versions of the design.

Future-Proofing Through Interoperable Design

Refactoring is not a one-time project; it is a shift toward a design philosophy that prioritizes interoperability from the start. As engineering software evolves toward cloud-native, AI-driven platforms, the ability to plug into a broader ecosystem becomes even more critical. Key trends to follow:

  • Open Standards Evolution: IFC 4.3 expands coverage to infrastructure projects like roads and railways. Adopting the latest versions ensures your refactored integration remains relevant.
  • Semantic Web and Linked Data: Using RDF/OWL to represent engineering domain knowledge allows heterogeneous tools to reason about data relationships automatically.
  • Digital Twins: A seamless data pipeline between design, simulation, and operational phases is essential for a living digital twin. Refactoring today lays the foundation for tomorrow’s IoT and real-time feedback loops.

By embedding interoperability into the architecture of your integration layer, you reduce the cost of adopting future tools and standards. Each new software suite can plug into the existing integration framework with minimal custom code.

Conclusion: A Strategic Investment

Refactoring for better interoperability between engineering software suites is not a trivial undertaking—it requires disciplined code restructuring, a focus on standards, and a willingness to retire legacy practices. Yet the payoff is substantial: faster workflows, lower error rates, and a more agile engineering organization. Start by auditing your most painful data exchange, choose one standard format or API approach, and refactor incrementally. Over time, these improvements compound, transforming your engineering environment from a collection of isolated tools into a unified, collaborative platform.

For teams unsure where to begin, resources like Martin Fowler’s Refactoring book and the ISO 10303 (STEP) community provide practical guidance. The path to interoperability is paved with small, well-tested refactorings—but the destination is a future where data flows freely and engineers focus on what matters: innovation.