AI Recodes Legacy Software to Operate on Modern Platforms

IBM's AI-based tools let engineers explore ways to extract value from legacy enterprise software

3 min read
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Last year, IBM demonstrated how AI can perform the tedious job of software maintenance through the updating of legacy code. Now Big Blue has introduced AI-based methods for re-coding old applications so that they can operate on today’s computing platforms.

The latest IBM initiatives, dubbed Mono2Micro and Application Modernization Accelerator (AMA), give app architects new tools for updating legacy applications and extracting new value from them. These initiatives represent a step towards a day when AI could automatically translate a program written in COBOL into Java, according to Nick Fuller, director of hybrid cloud services at IBM Research.

Fuller cautions that these latest AI approaches are currently only capable of breaking the legacy machine code of non-modular monolithic programs into standalone microservices. There still remains another step in translating the programming language because, while the AMA toolkit is in fact designed to modernize COBOL, at this point it only provides an incremental step in the modernization process, according to Fuller. “Language translation is a fundamental challenge for AI that we're working on to enable some of that legacy code to run in a modern software language,” he added.

In the meantime, IBM’s latest AI tools offer some new capabilities. In the case of Mono2Micro, it first analyzes the old code to reveal all the hidden connections within it that application architects would find extremely difficult and time consuming to uncover on their own, such as the multiple components in the underlying business logic that contain numerous calls and connections to each other. 

“Language translation is a fundamental challenge for AI that we're working on to enable some of that legacy code to run in a modern software language.”

Mono2Micro leverages AI clustering techniques to group similar code together, revealing more clearly how groups of code interact. Once Mono2Micro ingests the code, it analyzes the source and object code both statically (analyzing the program before it runs) and dynamically (analyzing the program while it’s running).

The tool then refactors monolithic Java-based programs and their associated business logic and user interfaces into microservices. This refactoring of the monolith into standalone microservices with specific functions minimizes the connections that existed in the software when it was a monolithic program, changing the application’s structure without altering its external behavior.

The objective of the AMA toolkit is to both analyze and refactor legacy applications written in even older languages (COBOL, PL/I). For the AMA toolkit, static analysis of the source code coupled with an understanding of the application structure is used to create a graph that represents the legacy application. When used in conjunction with deep-learning methods, this graph-based approach facilitates data retention as AMA goes through deep-learning processes.

mono2microAn image of the Mono2Micro interface.Image: IBM Research

IBM’s AI strategy addresses the key challenges for machine learning when the data input is code and the function is analysis: volume and multiple meanings. Legacy, mission-critical applications are typically hundreds of thousands to millions of lines of code. In this context, applying machine learning (ML) techniques to such large volumes of data can be made more efficient through the concept of embeddings.

These embedding layers represent a way to translate the data into numerical values. The power of embeddings comes from them mapping a large volume of code with multiple possible meanings to numerical values. This is what is done, for example, in translating natural human language to numerical values using “word” embeddings. It is also done in a graph context as it relates to code analysis.

“Embedding layers are tremendous because without them you would struggle to get anything approaching an efficiently performing machine-learning system,” said Fuller.

He added that in the case of code analysis, the ML system gets better in recommending microservices for the refactored legacy application by replicating the application functionality.

Fuller noted: “Once you get to that point, you’re not quite home free, but you’re essentially 70 percent done in terms of what you’re looking to gain, namely a mission critical application that is refactored into a microservices architecture.”

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Frank Stockton
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