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With application modernization a top priority for enterprises, staying competitive requires the intelligent use of AI-powered tools that accelerate and mitigate the risks associated with modernizing legacy applications. Here are nine essential tools and capabilities that can be used to drive successful application modernization:
1. AI-driven requirements capture
Use AI to reverse engineer business requirements directly from your existing codebase. This generates comprehensive technical artifacts such as ERDs, file maps, and C4 diagrams, providing a reliable foundation for any application modernization project.
2. Automated codebase analysis
Analyze your legacy codebase to identify critical components and dependencies. This reduces the risk of errors and downtime during modernization, resulting in a smoother transition to a more modern architecture.
3. AI-assisted project planning
Utilize AI to streamline project planning and help teams map out modernization initiatives with greater accuracy and efficiency. This enables faster decision-making and resource allocation.
4. AI-powered code generation
Turn business requirements into production-ready code with AI-driven code generation tools to accelerate the redevelopment of legacy applications and support rapid prototyping.
5. Legacy product documentation automation
Automatically generating and updating documentation for legacy systems helps bridge knowledge gaps, especially in environments with high developer turnover or limited knowledge transfer.
6. CoreStory’ GitHub Copilot extension
Teams can get much more from GitHub Copilot’s performance by feeding it with richer, project-specific context from CoreStory. The integration improves code suggestions and aligns them with your modernization goals, ultimately saving time and money.
7. Custom standards management
It’s vital to store, manage, and distribute your organization’s custom technical and operational standards to ensure AI-generated outputs are relevant, reliable, and compliant with internal policies.
8. Iterative modernization
Organizations should continuously update and adapt sensitive legacy applications using AI tools that support continuous improvement and minimize risk throughout modernization.
9. Business-to-code translation
Leverage AI to communicate goals in plain language and translate into technical specifications and deployable code. This bridges the gap between business and engineering for more effective application modernization.
By automating critical steps—from requirements capture to code generation and documentation—organizations set themselves up for success when it comes to modernizing legacy systems efficiently, reducing risk, and accelerating innovation.
