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Mainframe Code Modernization Patterns

In today’s enterprise digital landscape, mainframe modernization is no longer a back-office technical exercise. It has become a strategic initiative tied directly to innovation velocity, operational resilience, regulatory compliance and long-term competitiveness. Organizations are not abandoning the mainframe, but they are rethinking how code, architecture and platforms evolve to meet modern business demands.

Techstrong polled its community of infrastructure, DevOps and mainframe professionals to understand how organizations are approaching application modernization, code transformation and AI-assisted development on the mainframe. Our findings reveal that modernization is now a mainstream priority, with most enterprises balancing deep architectural change and operational stability while evaluating hybrid placement strategies and security-first platform decisions.

Techstrong brings you this PulseMeter report sponsored by BMC, supported by our independent research data. Learn more by downloading the full report.

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February 13, 2026
BMC CEO: Time for IT Teams to Shift Gears is Now

BMC CEO Aman Sayed is predicting that as the global economy after several years continues to stabilize, there are now enough signs to suggest the company year will see a wave of growth that will require IT teams to start shifting gears now if they hope to be prepared to meet new challenges.

Speaking this week at a BMC Connect 2024 event, there is now enough light at the end of tunnel to determine the next year will be one of recovery and growth. IT leaders, as a result, should expect to see a significant expansion in the number of initiatives their organizations are prepared to launch. In comparison, during the last few years organizations have been more focused on simply trying to survive during a sustained economic contraction that, as interest rates continue to decline, is starting to ease, noted Sayed.

IT teams, despite the complexity of their IT environments, are going to soon find there will be greater emphasis placed on the speed at which they can deliver new applications infused with artificial intelligence (AI) capabilities, said Sayed.

Providing the IT support required to help drive those initiatives will require organizations to rely more than ever on automation, because organizations are not going to hire armies of people to drive those initiatives, added Sayed.

The challenge is that much of the data needed to drive those applications is not only in multiple formats, but also strewn across multiple platforms. The IT world, by definition, is heterogeneous, noted Sayed.

An upturn in the economy bodes well for DevOps engineers that play a pivotal role in accelerating the pace at which applications are both developed and deployed. Many DevOps teams have spent a significant amount of time over the past few years optimizing and modernizing existing applications. but as the overall economy continues to improve, the number of new projects should steadily increase. Many of those projects will involve various types of highly custom language models that have been trained to automate tasks within specific domains, noted Sayed.

Of course, while the size of some DevOps teams might increase as a result, the general expectation is that IT organizations will be relying more on AI to build, deploy, secure and manage applications.

Each DevOps team will naturally adopt AI as they best see fit, but it’s no longer a question of whether to use AI as much as to what degree to rely on it. While AI coding tools make developers more productive, there are issues that need to be addressed before the code being created is thoroughly reviewed, before incorporating it into a production environment. AI platforms such as ChatGPT were trained using samples of code of varying quality collected from across the Web. In some instances, the code generated might therefore include a vulnerability that could be easily exploited.

Fortunately, the next wave of generative AI platforms is being trained on data for specific domains that have been closely vetted. Additionally, AI agents that have been trained to automate specific tasks will soon be incorporated into DevOps workflows. As these tasks become increasingly automated, DevOps teams will soon be orchestrating multiple agents that will enable them to build and deploy software at levels of scale that not too long ago would have been deemed unimaginable.

The issue now is determining how best to employ those capabilities in advance of a wave of economic growth that will require DevOps teams to automate many more tasks across the software development lifecycle (SDLC) than they already have.

October 18, 2024
BMC Brings Generative AI to the Mainframe

BMC, as part of a broader commitment to building generative artificial intelligence (AI) assistants to simply the management of mainframes, has made available in beta a tool that explains code functionality.

John McKenny, senior vice president and general manager for Intelligent Z optimization and transformation at BMC, said BMC Automated Mainframe Intelligence (AMI) DevX Code Insights will, via a chat interface, provide guidance to help debug code written in multiple languages, understand system processes and make better-informed decisions.

Based on large language models (LLMs) that BMC is training, BMC AMI DevX Code Insights is one of several AI agents that BMC plans to make available via a single console. Those LLMs will either be developed by BMC or based on third-party platforms. Alternatively, BMC also plans to enable organizations to invoke any LLMs they decide to build themselves.

BMC is also inviting organizations to participate in a Design Program through which the company will provide access to generative AI features as they are developed. In effect, AI agents will provide the equivalent of a subject matter expert (SME) for each task assigned, added McKenny. That’s critical because for generative AI to be effective, a platform needs to provide more than an ability to use prompts to ask questions, he noted. Instead, an AI agent should be able to surface guidance and insights that help streamline workflows, added McKenny.

While generative AI will play a major role in making it simpler to manage mainframes, they are only one component of an ongoing BMC effort to simplify the management of mainframe platforms for the next 30 years or more, said McKenney. For example, BMC will be enabling developers to self-service their requirements using a service catalog that reduces their dependency on IT operations teams, he noted.

Ultimately, the goal is to make the mainframe just another type of distributed computing platform that requires much less specialized skills to manage. BMC is not the only provider of software for the mainframe moving in that direction, but with the rise of generative AI, the amount of time required to achieve that goal is about to be substantially reduced.

That may prove crucial as the number of AI models being deployed on mainframe platforms where massive amounts of data already exist starts to steadily increase in the months and years ahead. After all, it’s usually easier to bring AI models to where data already exists than it is to move that data onto another platform.

IT teams might also want to revisit what types of workloads are running on what platform in IT environments. While not every IT organization has a mainframe, those that do can lower the total cost of IT by taking advantage of mainframe licenses that are designed to encourage IT teams to consolidate more workloads on the platform.

Regardless of approach, the one clear thing is that in time IT teams will become more homogenous, as in the age of AI it becomes simpler to manage workloads regardless of where they happen to be physically located.

August 2, 2024
BMC Adds More Tools to Modernize Mainframe Applications

BMC this week added a BMC AMI DevX Code Insights tool that makes use of machine learning algorithms to break apart monolithic COBOL mainframe applications into subprograms that are easier to update and refactor.

In addition, BMC is now making available BMC AMI zAdviser Enterprise, a commercial instance of a previously announced free tool that uses machine learning algorithms to collect telemetry data from mainframe applications. The commercial version of the tool adds a dashboard built and maintained by BMC that makes it simpler to consume that data.

John McKenny, senior vice president and general manager of Intelligent Z Optimization and Transformation at BMC, said BMC AMI DevX Code Insights visually maps a mainframe application to analyze data flow and debug applications made up of millions of lines of code in real-time that would otherwise be too complex to unravel. Armed with the insights gained, IT teams can then isolate business logic, refactor subprograms, add application programming interfaces (APIs) or remove dead code as they see fit, he added.

BMC AMI DevX Code Insights also makes it simpler for organizations to onboard new developers to a project at a time when many of the original developers of COBOL applications are retiring, said McKenny.

In the coming year, BMC is also committed to adding generative artificial intelligence (AI) tools to its portfolio for reengineering mainframe applications, added McKenny. Those tools, for example, will make it simpler to create documentation for legacy applications, he noted.

It’s not clear how quickly organizations that have mainframes are looking to re-engineer applications, but as more of them adopt DevOps best practices, they are being updated more frequently. The less monolithic the application, the simpler that task becomes. The issue is that mainframes tend to run large applications that access large amounts of data, so many IT teams are naturally fearful of breaking them if they are reengineered, noted McKenny.

While there is no doubt that the process of reengineering applications will become more automated in the years ahead, software engineers will still be needed to manage the overall process, said McKenny. The role of the software engineer will undoubtedly evolve, but it’s not likely software development and deployment will be wholly automated any time soon, he noted.

In the meantime, IT organizations that manage mainframes continue to embrace DevOps best practices to varying degrees. There may be some applications that they continue to build and deploy using waterfall methodologies, but as mainframe applications become integrated into digital business workflows, the need to update them faster becomes more apparent.

The challenge, as always, is not only providing IT teams with the tools to make that transition but also navigating the cultural issues that inevitably arise any time a change of this magnitude is made to the software development life cycle (SDLC). Regardless of the approach, the building and maintenance of mainframe applications are evolving. The only issue that remains to be resolved is finding ways to reduce the level of friction that transition creates as quickly as possible.

December 1, 2023
DevOps is Making Gains on Mainframe Platforms

A survey of 800 executive and technical professionals conducted by BMC found close to two-thirds (62%) are employing DevOps workflows to build and deploy applications.

In addition, the survey also finds that just under a third of respondents (32%) are applying artificial intelligence to IT operations (AIOps) across all platforms.

John McKenny, senior vice president and general manager of intelligent Z optimization and transformation at BMC, said the survey shows that organizations continue to invest in mainframe applications alongside distributed computing environments.

In fact, 61% of respondents noted that data volume on mainframes increased in the last year, with 56% reporting an increase in the number of databases running. Well over a third of respondents (35%) also reported increased interest in taking advantage of cloud technologies to handle, for example, storage and data protection.

Overall, the survey found that 94% of respondents have a positive perception of the mainframe, with nearly two-thirds (63%) reporting increased mainframe investments over the past year.

Compliance and security lead the list of priorities for mainframe organizations, with 61% of respondents citing these as top issues, followed by cost optimization (49%), enhancing automation (41%) and data recovery (40%).

In general, organizations with mainframes are adopting modern application development tools such as VS Code to attract a younger generation of developers. It’s critical to continue to attract developers to mainframe platforms that continue to run the most mission-critical applications in the enterprise, said McKenny. Many of those applications are being modernized using DevOps tools and practices rather than being moved to another platform, he added.

He added that the appetite for using generative artificial intelligence (AI) to write code for mainframe applications is less clear because of intellectual property concerns. Most organizations are not going to want to expose their mainframe code to a general-purpose large language model (LLM) that drives a public generative AI platform, but there will be a role for private LLMs that are trained using code that has been validated, noted McKenny.

Regardless of how generative AI evolves, there’s still much work to be done to further the adoption of DevOps best practices in mainframe environments. Each organization will determine how stringently to apply those practices based on the frequency at which applications running on mainframes need to be updated. In most cases, organizations with mainframes will use a mix of waterfall and DevOps-based approaches to building and deploying applications for many years. The decision as to which approach to employ will, naturally, have as much to do with culture as it does the rate at which the application needs to be built and deployed to drive, for example, a digital business transformation initiative.

The days when mainframes were managed in isolation from the rest of a distributed computing environment are slowly coming to an end. A mainframe is, arguably, just another node for deploying applications. The challenge and the opportunity now is getting the right DevOps tools in the hands of the IT team managing heterogeneous IT environments, including mainframes.

September 15, 2023
BMC Adds Generative AI Capabilities to Helix Management Platform

BMC today revealed it is embedding generative artificial intelligence (AI) capabilities across its BMC Helix service and operations management platforms.

BMC CTO Ram Chakravarti said the company is incorporating the open source GPT-J large language models (LLMS) to build generative AI models. Those models are optimized for specific domains using data collected by BMC and imported from other data sources, like the Jira project management software from Atlassian. That persistent layer of storage is designed to aggregate multiple types of data within what amounts to a Helix data lake.

The generative AI capabilities added to BMC’s portfolio include natural language summaries of issues and events captured by BMC Helix Operations Management, along with an ability to create a historical analysis of a user’s specific environment to identify recurring issues more easily.

BMC Service Management, meanwhile, can now correlate incidents and provide resolution summaries that can be stored. It’s also now possible to run clustering analysis on chat archives to find new or improved utterances that help virtual agents answer queries better and execute workflows.

BMC initially opted to build its own LLMs because general-purpose LLMs—such as the one created by OpenAI—are subject to “hallucinations.” Hallucinations are created when incorrect data is included in a model. BMC is training its LLMs on a narrow range of trusted data to ensure the recommendations and subsequent automations it creates are valid.

Over time, BMC will add additional LLMs; the company hasn’t yet decided if there are use cases where it makes sense to rely on an existing general-purpose LLM to add additional capabilities, noted Chakravarti.

Organizations can also opt to use a containerized version of the BMC LLM if they prefer, but BMC will encourage most organizations to invoke generative AI via its Helix SaaS platform, he added.

BMC Helix is an IT services management (ITSM) platform that integrates with a range of DevOps platforms. Helix is part of a larger existing BMC Connected Digital Ops initiative that seeks to unify ServiceOps and DevOps with AIOps, DataOps and AutonomousOps. Generative AI will automate a range of tasks from within Helix that can then be extended to DevOps platforms such as Jenkins.

It’s still early days as far as applying generative AI to processes, so organizations should focus on use cases where it will add some immediate value, said Chakravarti. Once they develop a level of trust in the automation created and executed, they can then pragmatically expand the number of generative AI use cases where they make sense, he added.

One way or another, generative AI is going to be applied to the management of IT. Many routine tasks that conspire to limit the productivity of IT teams today will be eliminated, but there will always be a need for a human to remain in the loop to ensure the right outcomes are achieved. However, it will also soon become possible for existing teams to manage IT at levels of scale that were previously unattainable.

Image source: Photo_Credite_Indira-Tjokorda via Unsplash

June 14, 2023
BMC Extends Scope of ServiceOps Initiative

At the BMC Exchange conference this week, BMC revealed it has updated its Helix IT service management (ITSM) platform to include mapping and discovery capabilities. Those updates make it simpler for cross-functional teams to correlate incidents, alerts and data and pinpoint root causes faster.

In addition, BMC is now applying pre-trained artificial intelligence (AI) models to observability data and the service models it tracks to assess service health, predict service impacts and proactively manage IT environments as the volume of metrics, events, and alerts continues to increase.

Finally, BMC also announced that the BMC Helix Control-M orchestration engine now supports open source Apache Airflow, a workflow management platform for defining data pipelines using a Python programming tool.

Margaret Lee, vice president and general manager for digital service and operations management at BMC, said these updates extend a unified ServiceOps initiative the company launched last year to unify ITSM, DevOps and customer service desk workflows. While each of these disciplines may have distinct objectives—such as speed and reliability—that are often in conflict with one another, Lee said it’s apparent IT organizations need to reduce the current level of friction that is often encountered when managing IT.

As a software-as-a-service (SaaS) platform, Lee said the BMC Helix platform achieves that goal via a consumer-like user interface that can be easily personalized. The goal is to make it simpler to create virtual agents, knowledge bases, live chat and tickets that make it easy for customers and employees to request IT help, she added.

Achieving that goal, however, will require IT organizations to rely more heavily on AI models to automate routine tasks as IT environments become more complex. There are simply not enough IT professionals available to manage IT environments using legacy processes, she noted.

In addition, end users and customers are expecting service requests to be automated in way that allow them to resolve issues via a mobile device, added Lee. In many cases, how cumbersome IT processes now has a direct impact on both customers and employee retention. The primary goal is to reduce the number of interactions required to complete a task such as changing a password or provisioning a laptop whenever a new employee needs to be onboarded.

While the pace at which IT management is evolving will vary greatly from one organization to the next, it’s apparent that many DevOps principles are being absorbed into traditional ITSM platforms. The challenge is that cultural divide between DevOps teams and centralized IT teams is still very wide. Most organizations are still trying to determine how far left they want to shift responsibilities toward application development teams versus continuing to rely on IT administrators using graphical tools to centrally manage IT workflows. It’s rarely going to be an all-or-nothing proposition. Rather, the issue is finding a way to strike a balance that enables organizations to be as agile as possible without introducing unacceptable levels of risk to resiliency.

October 26, 2022
BMC Survey Surfaces DevOps Mainframe Progress

A global survey of 1,000 executives and IT professionals that rely on mainframes found 70% are now employing DevOps processes to build and deploy applications on the venerable platform, with 60% of those respondents now deploying applications faster. A full 80% of respondents also wanted to deploy mainframe applications faster, according to the survey.

The survey also found that integration of IT processes across mainframes and distributed computing environments has increased, with well over half (57%) now sharing platform operations roles. A total of 44% reported site reliability engineering (SRE) as a shared role that spans both platforms.

Applications developed in-house are being delivered faster than a year ago, reinforcing the mainframe’s business value, with 76% realizing value in under 12 months.

More than half of respondents (53%) said their organizations is also implementing DevSecOps practices in a way that spans all those platforms. The top priority for organizations that have mainframes is security and compliance (67%), the survey also found.

Nearly a third have also adopted artificial intelligence for IT operations (AIOps) across mainframes and distributed computing platforms. Survey respondents that have adopted both mainframe AIOps and DevOps reported they are gaining visibility into service issues 70% faster, with 41% improving uptime and availability, according to the survey.

Finally, the survey found 95% of respondents still had a positive perception of the mainframe as a platform for running applications, with 62% reporting transaction volumes on the mainframe are increasing. More than half (55%) also noted workload volatility and unpredictability is increasing. A similar percentage (55%) also reported more than half of their data is on a mainframe.

John McKenny, senior vice president and general manager for Intelligent Z mainframe optimization and transformation at BMC, said IT teams in a downturn are, as usual, trying get the most out of their existing investments. The thing that is changing most is the mainframe is now becoming more integrated with distributed computing environments as IT teams strive to achieve that goal, he added.

In general, there’s still much work to be done in terms of furthering adoption of DevOps best practices in mainframe environments, noted McKenny. Each organization will most likely determine at what level to apply those practices based on the frequency at which applications running on mainframes need to be updated. There are still plenty of applications running on mainframes that might not need to be updated for many years, but as organizations embrace digital business transformation initiatives, the need to update mainframe applications more frequently has increased, noted McKenny.

Regardless of the type of application deployed, it’s apparent that the days when mainframes were managed in isolation from the rest of a distributed computing environment are slowly coming to an end. A mainframe is, arguably, just another node for deploying applications. The one major difference is that, as platforms are considered, the mainframe is a lot more secure, said McKenny.

The challenge and the opportunity now, of course, is as much about changing attitudes toward the mainframe as it is making the right DevOps tools available to the IT teams tasked with managing heterogeneous IT environments.

October 10, 2022
BMC Adds Support for DORA Metrics to Mainframe Tools Portfolio

BMC this week announced it has added support for DevOps Research and Assessment (DORA) metrics within its portfolio of DevOps tools for mainframe environments.

John McKenny, senior vice president and general manager for intelligent Z optimization and transformation at BMC, said the latest edition of BMC Compuware zAdviser now includes a DORA KPI Dashboard that aligns with the widely-adopted framework for measuring DevOps efficiency defined by an arm of Google.

The DORA metrics track deployment frequency, failure rate, lead time for changes and mean-time-to-recovery (MTTR). Tracking those metrics is crucial for IT teams that have mainframes because they need to make sure applications continue to be deployed on the platform versus being usurped by another platform that is perceived to provide a faster alternative for builds and deployment, noted McKenny.

BMC also added support for automated webhook notifications to its BMC Compuware Abend-AID offering. That includes diagnostic and root cause analysis to resolve defects in test and production faster. In addition, BMC provides a connector to the machine-identity management platform created by Venafi. That integration promises to eliminate certificate-related outages that can derail an application development initiative.

In general, DORA metrics are being used to identify areas where DevOps teams can improve. It is up to each organization to determine to what degree those metrics will be used to coach teams versus enforce productivity mandates. However, given the overall shortage of available DevOps talent, few organizations are employing metrics to force DevOps teams to improve. In fact, there is no universal set of DevOps goals that every organization is trying to achieve. Rather, most organizations are trying to determine what set of DevOps best practices makes the most sense to implement given their own goals and internal culture. In the case of the mainframe, most organizations start with a few projects before they begin to employ DevOps more widely, noted McKenny.

The metrics defined by DORA framework are, of course, only a subset of the total metrics that DevOps teams should track. BMC, for example, makes it possible to track tools usage to provide DevOps teams with insights into what tools may no longer be relevant or required, noted McKenny.

Mainframe environments also have a long history of using waterfall-based approaches to building and deploying applications that many of them will continue to use alongside DevOps practices, depending on the type of application being built and deployed. In other instances, distributed applications built using DevOps practices are being integrated with mainframe applications that now need to be updated using the same workflow. The mainframe is no longer a platform that is managed in complete isolation from distributed computing environments.

The major challenge is that many organizations using mainframes often overestimate how difficult it is to adopt DevOps best practices, McKenny said. Regardless of the platform, there is no doubt the overall rate at which all applications are being built and deployed will continue to increase. The mainframe is proving to be no exception to that rule.

July 8, 2022
BMC Integrates DevOps Mainframe Portfolio With Git

BMC has expanded its efforts to bring DevOps best practices to the development of mainframe applications by integrating the BMC Compuware ISPW continuous integration/continuous delivery (CI/CD) platform with Git repositories.

In addition, the company tightened integration between BMC Compuware ISPW and BMC AMI Ops platform and integrated BMC Compuware Topaz Workbench for building applications with code scanning tools from Vercacode.

Finally, there is also now a BMC Enterprise Connector for Illumio that provides a bi-directional interface between the zero-trust segmentation platform from Illumio and the mainframe. In addition, the BMC AMI Command Center for Security system can now also surface malicious performance activity to accelerate incident response.

John McKenny, senior vice president and general manager for intelligent Z optimization and transformation at BMC, said the overall goal is to make the mainframe just another platform where IT teams can apply DevOps best practices to build applications faster. That’s more critical than ever because mainframes are often at the core of digital business transformation initiatives and require integration with a wide range of distributed computing platforms. A recent survey published by BMC found two-thirds of respondents (66%) are investing in new technologies such as AIOps and artificial intelligence/machine learning (AI/ML), DevOps and integrations.

That approach enables organizations to leverage existing mainframes in a way that minimizes technical debt rather than duplicating functionality, added McKenny.

When it comes to DevOps in mainframe environments, the biggest challenge these days has more to do with culture than the maturity of the tools being provided, he said. Many of the IT teams building mainframe applications still rely on waterfall processes mainly because of simple inertia and a general fear of change, McKenny noted.

There is, however, a new generation of IT professionals that have embraced modern development practices across all platforms to accelerate the rate at which applications are built and deployed, said McKenny. That shift is leading to the wider adoption of DevOps best practices within both mainframe and distributed computing environments, he added. In fact, as a result, enterprise IT has never been more hybrid, he noted.

IBM continues to invest in mainframes, so the platform should remain relevant well through this decade and into the next. Though it’s been nearly 60 years since the venerable platform was introduced, it continues to host mission-critical applications for a broad range of enterprise IT organizations. Most recently, IBM added an IBM z16 edition of the mainframe that adds a dedicated on-chip AI accelerator in addition to quantum-safe cryptography. It’s not clear how quickly organizations that have mainframes will upgrade, but that level of investment suggests IBM is still investing heavily in mainframe research and development.

Regardless of how—or even who—builds applications for the mainframe, tools such as Git repositories will be more widely used if for no other reason than to more easily share code. Now, the issue is improving the overall level of DevOps maturity within mainframe environments to keep up with the rapid pace at which applications are being built and updated across the rest of the enterprise.

April 29, 2022