Automate application refactoring with AI.

Today, only 20% of enterprise workloads are in Cloud, and they were predominately written for cloud architectures. This leaves 80% of legacy applications on-premises, waiting to be modernized for the cloud.

We know that the best way to modernize your business-critical application is to refactor it into microservices—this approach allows microservice to be independently enhanced and scaled, providing agility and improved speed of delivery. IBM’s novel AI technology automates the application refactoring with minimal risk and removes the need for any major rewrite.

Introducing IBM Mono2Micro

Application refactoring is the process of restructuring existing code without changing its external behavior and semantics. Currently, refactoring is usually done manually and is expensive, time-consuming, and error-prone.

We are excited to announce IBM Mono2Micro, which helps you accelerate this journey to cloud by automating the process of application refactoring with AI.

Mono2Micro is based on IBM Research technology that, when applied to the application code and runtime, traces reasons about application behavior, extracts the business logic, and identifies optimal microservice candidates. Microservice recommendations are automatically generated, while taking programming model and application data dependencies into account. The approach minimizes the risk of refactoring and any requirements for significant code rewrite thereby providing you with a huge ROI.

Business logic-based groupings

Mono2Micro analyzes runtime call traces in the context of the business functions they support, which exposes how classes interact, in what sequence, and at what frequency. The underlying artificial intelligence techniques—such as deep learning and machine learning—generate business logic-based class groupings of the runtime call traces to capture causality, functional similarity, and other temporal relations among classes and their methods.

Data dependency and natural seams-based grouping

Mono2Micro further augments the business logic-based groupings with data dependency analysis. It iteratively merges relevant groupings of classes with data dependencies to generate natural seams-based groupings. With these groupings, Mono2Micro minimizes the need to rewrite existing classes.

Overall, Mono2Micro provides a multifaceted view of your monolith-to-microservice refactoring. It can help you understand and arrive at informed and assured decisions on transforming your current applications.

Next steps

Learn more about Mono2Micro and modernizing your applications:

More from Artificial intelligence

Responsible AI is a competitive advantage

3 min read - In the era of generative AI, the promise of the technology grows daily as organizations unlock its new possibilities. However, the true measure of AI’s advancement goes beyond its technical capabilities. It’s about how technology is harnessed to reflect collective values and create a world where innovation benefits everyone, not just a privileged few. Prioritizing trust and safety while scaling artificial intelligence (AI) with governance is paramount to realizing the full benefits of this technology. It is becoming clear that…

Taming the Wild West of AI-generated search results

4 min read - Companies are racing to integrate generative AI into their search engines, hoping to revolutionize the way users access information. However, this uncharted territory comes with a significant challenge: ensuring the accuracy and reliability of AI-generated search results. As AI models grapple with "hallucinations"—producing content that fills in gaps with inaccurate information—the industry faces a critical question: How can we harness the potential of AI while minimizing the spread of misinformation? Google's new generative AI search tool recently surprised users by…

Are bigger language models always better?

4 min read - In the race to dominate AI, bigger is usually better. More data and more parameters create larger AI systems, that are not only more powerful but also more efficient and faster, and generally create fewer errors than smaller systems. The tech companies seizing the news headlines reinforce this trend. “The system that we have just deployed is, scale-wise, about as big as a whale,” said Microsoft CTO Kevin Scott about the supercomputer that powers Chat GPT-5. Scott was discussing the…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters