As organisations are looking to capitalise on the potential value that innovative technologies can add, IT leaders are facing increasing pressure to modernise legacy systems, while maintaining operational continuity. Traditional modernisation efforts are costly, complex, and time-consuming. Generative AI is a technology that can help in simplifying legacy modernisation, offering solutions that go beyond what traditional techniques can achieve.
This article explores how generative AI specifically enhances various stages of legacy modernisation, from automated code generation and documentation to testing and data migration. Additionally, we look at how IT leaders can mitigate risks associated with AI adoption, and present quantifiable metrics to demonstrate the value generative AI delivers in legacy system transformations.
Generative AI vs Traditional AI in legacy modernisation
Those familiar with AI may wonder how generative AI differs from traditional machine learning models or automation tools often used in modernisation projects.
Capabilities of Generative AI:
Automated Code Writing:
Unlike traditional AI models, generative AI can autonomously generate new code that fits the context of the existing system. Traditional AI may assist in identifying bugs or analysing performance but lacks the ability to actively write or refactor code.
Complex Documentation Generation:
Generative AI tools can analyse legacy codebases and automatically produce detailed system documentation that previously required significant manual effort. This level of automated insight generation is unattainable with conventional AI, which may only provide surface-level analytics or reports.
Adaptability Across Languages and Frameworks:
Generative AI is capable of translating code between different languages and frameworks. For instance, tools like OpenAI GPT-4 and Claude 3.5 can refactor COBOL into Python or convert monolithic codebases into modular microservices architectures. Traditional AI solutions are typically limited to assisting with predefined tasks, such as optimising code performance, rather than actively transforming it.
Where can Generative AI add value?
Documentation
Legacy systems often come with poorly documented codebases, convoluted business logic, and outdated architectures, making modernisation projects extremely difficult. Generative AI models, such as large language models (LLMs), can analyse complex codebases and generate detailed documentation that was previously unattainable.
AI-Generated Documentation
Generative AI tools like GitHub Copilot or OpenAI GPT-4 and Claude 3.5 can automatically generate comprehensive system-level documentation by interpreting the legacy code. These AI-driven tools can:
- Provide function-level summaries and code comments that were missing or incomplete.
- Map out interdependencies between various modules, databases, and business processes.
- Identify dead code or areas of the system that are no longer used but still contribute to technical debt.
For example, modernising a 30-year-old COBOL-based system using AI to automatically generate documentation that describes core transaction workflows, database schemas, and security protocols, enabling teams to understand the system’s intricacies, which may have been lost after decades of maintenance by multiple generations of developers.
Code Refactoring
One of the most labour-intensive parts of modernising legacy systems is refactoring outdated code to align with modern standards and frameworks. Generative AI can significantly reduce this burden by automating large parts of the refactoring process, improving code quality.
AI-Assisted Refactoring
Generative AI tools like OpenAI ChatGPT or Tabnine can analyse legacy codebases and suggest refactoring opportunities. These tools are capable of:
- Converting monolithic codebases to microservices architectures, breaking down large blocks of procedural code into smaller, reusable components.
- Automatically transforming outdated code, such as COBOL into modern languages like Python or Java, while preserving the underlying business logic.
- Identifying and removing redundant or duplicate code, which not only simplifies the system but also improves performance.
For example, modernising a legacy system written in COBOL using an AI tool that translates business-critical code into Python, automating the translation whilst also highlighting sections of the code that were no longer necessary, helping to reduce the overall system size.
Test Generation and Automation
Testing legacy systems is a crucial but often overlooked part of modernisation. Most legacy systems lack robust automated testing frameworks, making it difficult to ensure that changes won’t break critical functionality. Generative AI can help generate tests automatically, significantly speeding up the process and improving code reliability.
Automated Unit and Integration Testing
AI-driven tools can analyse a legacy system’s code and automatically generate:
- Unit tests that validate individual functions or components.
- Integration tests to ensure that different parts of the system work together seamlessly.
- Regression tests to verify that new code does not introduce bugs into the system.
For example, using AI to generate unit tests for a legacy system to save time and also identify edge cases that manual testing would have missed, reducing the overall number of post-deployment bugs.
Data Migration and Structuring
Data migration can be one of the riskiest and most technically challenging aspects of legacy modernisation projects. Data in legacy systems is often unstructured, inconsistently formatted, or incompatible with modern architectures. AI can help simplify this process.
AI-Powered Data Mapping and Migration
AI tools like IBM Watson or Google’s Data Migration Service can automatically:
- Map legacy data structures to modern database schemas, reducing the need for manual intervention.
- Ensure data integrity by validating mappings and checking for inconsistencies.
- Assist in real-time data migration with minimal disruption to ongoing operations.
For example, using AI for data mapping in migrating a vast amount of records from a legacy system to a modern platform, ensuring all data is correctly formatted, compliant with GDPR, and preserved throughout the process.
APIs and Middleware
Many legacy systems must remain operational even as modernisation efforts proceed, requiring integration with modern platforms. AI can help this process by automatically generating APIs that connect legacy systems to modern environments. For example:
- RESTful API generation can be automated for legacy mainframes, allowing these systems to communicate with modern cloud-based platforms or microservices architectures.
- AI tools like IBM Watson or OpenAI GPT-4 can automatically create middleware that translates data between incompatible systems, ensuring smooth data flow without requiring significant manual coding efforts.
This type of API generation is particularly useful in industries where legacy systems need to remain operational while new systems are gradually introduced. In such cases, generative AI ensures that integration is seamless, without manual coding bottlenecks that often delay modernisation efforts.
For example, using AI to integrate a legacy production monitoring system with an IoT-based predictive maintenance platform, with an AI-generated API allowing the legacy system to feed data into the cloud platform in real time, enabling predictive analytics to reduce downtime.
Risk Mitigation for AI in Legacy Modernisation
Ensuring Quality of AI-Generated Code:
A critical concern in using AI in this way is ensuring that AI-generated code is of high quality and does not introduce instability or inefficiencies into the system. One way to mitigate this risk is through integrating static analysis tools that evaluate code complexity, adherence to standards, and potential security vulnerabilities. For example:
- Static code analysis tools like SonarQube or Checkmarx can automatically scan AI-generated code to identify problematic patterns before the code is deployed into production environments.
- Teams may also use AI outputs for rapid prototyping, using AI-generated code to create an initial solution that can then be manually refined and optimised by human engineers.
Challenges of AI Interpretability and Transparency:
Generative AI models often act as "black boxes," making it difficult to fully understand how they arrive at certain solutions. This lack of transparency can pose risks, especially in industries with strict compliance or regulatory standards, like healthcare or finance. To address this:
- Teams should maintain visibility and control over AI-driven refactoring by coupling AI with tools that offer traceability, such as Git for version control and CI/CD pipelines for continuous monitoring of AI-suggested changes.
- Explainable AI (XAI) techniques are emerging to help developers better understand AI decision-making processes. XAI tools can be integrated into AI workflows to gain insights into why specific code changes were suggested or implemented, improving trust in the system.
Limitations of Generative AI in Specific Scenarios:
Generative AI, while powerful, is not a universal solution for all legacy modernisation projects. It may struggle with:
- Highly customised systems where business logic is deeply embedded and not easily discernible by AI models. In such cases, human expertise is necessary to ensure that critical functionalities are preserved during the modernisation process.
- Complex interdependencies between legacy systems and external applications, particularly where APIs or data flows are undocumented or ad hoc. While AI can assist with generating documentation, the process may require significant manual input to fully capture the intricacies of these systems.
By recognising these limitations, teams can better plan when and where to apply generative AI, using it to complement human efforts rather than fully replace them.
Measuring the Impact of Generative AI in Legacy Modernisation
IT leaders are often tasked with justifying investments in new technologies, especially those like AI that require upfront costs. Providing quantifiable metrics for generative AI’s impact on modernisation projects will make the case for AI adoption more compelling.
Example Metrics:
- Reduction in code refactoring time: Generative AI can reduce the time required to refactor legacy codebases by up to 30%. By automating repetitive and manual refactoring tasks, developers can focus on higher-level architectural decisions.
- Automated test generation efficiency: AI-driven test generation can reduce the number of undetected bugs by 30% during regression testing. Automated test case generation not only covers more edge cases but also ensures continuous testing throughout the development pipeline.
- Faster documentation generation: Generative AI tools can speed up the creation of technical documentation by 45–50%, saving developers countless hours in understanding and mapping legacy systems.
By presenting these metrics, it is possible to see clear, quantifiable benefits of integrating generative AI into modernisation projects.
Generative AI offers a new approach to simplifying legacy modernisation projects by automating previously manual, error-prone tasks like code generation, testing, and API development. While risks remain, they can be mitigated through careful planning, human oversight, and the integration of static analysis and monitoring tools.
IT leaders should take a phased approach to generative AI integration, starting with non-critical systems to validate the technology’s impact before scaling to mission-critical applications. With AI, legacy systems can be modernised faster, more efficiently, and with fewer disruptions, unlocking the full potential of digital transformation.
