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Whitepaper
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Legacy systems present significant challenges for government agencies. Without active development teams or institutional memory, these systems become difficult to maintain and update—especially when addressing critical security flaws.
Government furnished equipment (GFE) and information (GFI) often arrive in poor condition, forcing contractors to build risk into bids and increasing costs. Documentation, when it exists, is typically outdated and doesn’t match current system configurations. This creates substantial challenges when modernization is required.
Application reconnaissance demands expensive senior developers to deconstruct codebases, analyze source control artifacts, and review system logs. Without proper regression testing, any changes risk breaking critical functionality.
Government management teams must establish and strictly govern three foundational process control elements:
These controls, combined with actionable elements from the following sections, form the complete GFE/GFI that the government archives between contractor maintenance periods.
Success requires first understanding the legacy system, then controlling it.
Understanding legacy applications requires knowing three key aspects:
Once teams understand these details, they can control and archive the information. When new contractors step in to evolve applications, they can quickly reconstitute the application along with its operational ecosystem.
Modern AI-enabled test automation solutions help implement legacy modernization as a manageable, cost-effective venture for government agencies.
Static analysis scans provide in-depth views into code from structural, maintainability, security, and safety perspectives. Security and safety analysis identifies potential runtime vulnerabilities by semantically parsing the integrated application, looking for buffer read/write issues, memory infractions, database infractions, and more—all of which could manifest as catastrophic production failures.
Apply multiple static analysis tools enforce industry standards including MISRA, OWASP, CWE, and others. Each vendor has different strengths, so running several tools provides comprehensive coverage. Incorporate static analysis into fully automated DevSecOps CI/CD processes, collecting ongoing compliance and trends reporting.
From source code, create a suite of "poor man’s regression tests" covering all application code. AI-enhanced test generation rapidly creates extensive test cases, isolating low-level code functionality, setting the stage for monitoring code changes. This works by analyzing function signatures and automatically creating test cases with extreme conditions.
The test suite will produce mixed results—some tests fail, some behave unexpectedly, some pass. The test team verifies expected results and converts correct tests into validated regression tests.
This suite tracks new outputs against past outputs as code evolves, answering critical questions:
Techniques like test impact analysis (TIA) can be applied during active development to ensure modified code does not impact existing functionality before it is committed to source control. Teams can further leverage TIA in their CI/CD pipelines to focus regression testing efforts, mapping code changes to impacted test cases automatically. This reduces the scope of the number of regression test cases that are executed each build and enables fast feedback loops.
Not all systems have user interfaces (UI), but many do. Selenium is a major tool for automating user functional testing for web-based applications, making it valuable to build regression suites of user workflows.
Many organizations already have Selenium test suites in place. For those focused on manual testing, leveraging automated Selenium test generation—using the same traffic recording techniques described earlier—provides significant productivity acceleration.
Selenium tests are notoriously brittle and difficult to maintain. Leveraging AI/ML to self-heal broken Selenium tests at runtime dramatically reduces the maintenance timelines and effort for tests involved in comprehensive system understanding.
AI and ML accelerate test generation, execution, maintenance, and analysis across all testing layers. At the unit level, AI automatically generates missing test cases. At the API layer, AI tracks patterns and relationships to help developers create advanced scenarios and system parameter models, while agentic AI making enables test creation through natural language instructions.
For Selenium tests, AI learns patterns and makes tests self-healing as code changes. AI also creates flags for performance changes associated with application updates.
Modern test infrastructure should integrate with CI/CD processes that control and manage application assembly, testing, and release. The entire CI/CD infrastructure, combined with underlying code and automated test suites, defines your legacy system going forward. This entirety is what gets archived for future use and reactivated for evolution.
These modern testing practices enable understanding that eventually moves into application evolution and modernization. The traceability from all testing practices directly to underlying source code accelerates modernization efforts.
Modernization is evolution—code will be added or updated. When dealing with complex applications and systems, the total number of tests can be overwhelming. TIA determines the minimum test set execution required to exercise just the updated code.
TIA saves significant time and operational costs in DevOps pipelines. Its fine-grained understanding of the application provides greater insight and control of ongoing evolution. For new teams adopting well-understood systems, modernizing efforts become easier, faster, and more cost-effective using the same solutions developed for understanding.
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