Using generative AI to modernize mainframe apps
If you take a closer look at any well-designed mobile application or business interface, you will probably discover mainframes operating underneath the integration and service layers of any significant enterprise’s application architecture.
As part of a hybrid infrastructure, these fundamental systems are used by critical applications and systems of record. The business’s ability to maintain its operational integrity might be severely jeopardized by any disruption to its current operations. So much so that many businesses are reluctant to alter them significantly.
But change is unavoidable, since technological debt is mounting up. Businesses must upgrade these apps in order to achieve company agility, meet consumer demand, and stay competitive. Leaders should look for innovative methods to speed up digital transformation in their hybrid strategy rather than putting it off.
COBOL is not to responsible for modernization delays
The shortage of skills is perhaps the largest barrier to mainframe modernization. Many of the mainframe and application specialists who throughout the years have built and extended business COBOL codebases are probably retired or have moved on.
Even more concerning, it will be difficult to get talent from this next generation since graduates of computer science programs that emphasize Java and other modern languages would not automatically think of themselves as mainframe application developers. They may not find the task as exciting or agile as cloud native programming or mobile app design. In many respects, this is a really unfair bias.
Long before object orientation, much alone cloud computing or service orientation, existed, COBOL was developed. It shouldn’t be a difficult language for less experienced developers to learn and comprehend since it has a small collection of instructions. Furthermore, there is no reason why agile development and more frequent, smaller releases inside an automated pipeline akin to DevOps wouldn’t be advantageous for mainframe programs.
Change management is particularly challenging when one has to figure out what various teams have done with COBOL throughout the years. An limitless number of components and logical loops have been added by developers to a procedural system that has to be updated and tested as a whole, not as individual parts or loosely connected services.
Interdependencies and possible points of failure are too complicated and many for even experienced developers to go through when code and applications are weaved together on the mainframe in this way. Because of this, developing COBOL apps might seem more difficult than necessary, leading many businesses to search for solutions outside of the mainframe before they should.
Overcoming generative AI’s constraints
Due to the increasing availability of consumer-grade visual AI picture generators and large language models (LLMs) like ChatGPT, generative artificial intelligence, or GenAI, has been the subject of several hypes recently.
Even though there are a lot of exciting new possibilities in this field, there is a persistent “hallucination factor” with LLMs when used in crucial business processes. Artificial intelligence (AI) systems that have been trained on online material often provide results that are somewhat correct but nonetheless persuasive and credible.
For example, ChatGPT recently used fictitious case law precedents in a federal court;
The indolent attorney who utilized it may face consequences. Putting your faith in an AI chatbot to write business application code raises comparable concerns. While a generic LLM may make fair general ideas for how to develop an app or simply churn out a standard registration form or build an asteroids-style game, the functional integrity of a business application relies greatly on the machine learning data the AI model was trained with.
Thankfully, production-focused AI research was underway long before ChatGPT made its debut. As the creator and pioneer of the mainframe, IBM has been developing deep learning and inference models under the watsonx brand. They have also developed observational GenAI models that have been trained and adjusted on COBOL-to-Java transition.
Their most recent IBM Watsonx Code Assistant for Z solution speeds up the modernization of mainframe applications by combining generative AI with rules-based workflows. Development teams may now rely on a very useful and enterprise-focused application of automation and GenAI to help with auto-refactoring, application discovery, and COBOL-to-Java conversion.
Three approaches to modernizing mainframe applications
Organizations should elevate mainframe programs to the level of top features in the continuous delivery pipeline so that they are as flexible and adaptable to change as any other distributed or object-oriented application.
Through three phases,
IBM Watsonx Code Assistant for Z assists developers in integrating COBOL code into the application modernization lifecycle:
1.Finding Developers: must determine which areas need care before updating. The method begins by listing every program on the mainframe and creating architecture flow diagrams that include all of the data inputs and outputs for each application. The visual flow model makes it simpler for developers and architects to detect dependencies and evident dead ends within the code base.
2.Refactoring: The main goal of this phase is to disassemble monoliths into more palatable forms. IBM Watsonx Code Assistant for Z searches through extensive program code bases to determine the system’s intended business logic. The method refactors the COBOL code into modular business service components by eliminating the connection between instructions and data, such as discrete operations.
3.Transformation: This is where an LLM optimized for corporate COBOL-to-Java translation may work its magic. The GenAI approach converts Java classes from COBOL program components, enabling real object orientation and concern separation to support agile, concurrent development by many teams. The AI then provides look-ahead ideas, much like a co-pilot feature you might find in other development tools, so developers can concentrate on improving Java code in an IDE.
The Intellyx perspective
As most vendor promises regarding AI are really automation under a different name, they tend to be wary about them.
Gaining proficiency in the syntax and structures of languages like COBOL and Java appears to be more in line with GenAI’s interests than studying every detail of the English language and conjecturing about the veracity of words and paragraphs.
The world’s most resource-constrained firms may save money and effort on modernization with the help of generative AI models created specifically for businesses, such as IBM Watsonx Code Assistant for Z. Recursive AI models such as IBM Watsonx Code Assistant for Z find perfect training grounds in applications on well-known platforms with thousands of lines of code.
GenAI can assist teams overcome modernization obstacles and enhance the skills of even more recent mainframe developers to create major advances in agility and resilience on top of their most important core business applications, even in circumstances with limited resources.
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