In the dynamic landscape of software development, where mainframe systems continue to play a critical role, the adoption of artificial intelligence (AI) is revolutionizing traditional practices. Specifically, the emergence of generative AI presents a paradigm shift in how developers interact with mainframe applications within the DevOps ecosystem. This article delves into the fundamental disparities between using generative AI and conventional AI methodologies in mainframe DevOps and explores how the former augments the developer experience.

The overarching goal of employing generative AI in mainframe DevOps is to enhance the application developer journey. Generative AI operates on the premise of explaining, guiding, and testing mainframe application changes and enhancements. Unlike traditional AI and machine learning (ML) techniques, which predominantly focus on optimizing system performance or detecting anomalies, generative AI takes a proactive stance in empowering developers throughout the software development lifecycle.

Understanding the scope of AI capabilities

Before delving into the nuances of generative AI, it’s important to delineate the capabilities of AI in the mainframe DevOps domain. AI encompasses a spectrum of techniques, ranging from anomaly detection and predictive maintenance to optimization and automation. These methodologies excel in augmenting system monitoring, maintenance, and performance. However, traditional AI falls short in addressing the intricate nuances of the developer journey, such as code comprehension, adherence to coding standards, and efficient testing strategies.

Why generative AI is the optimal solution

Generative AI emerges as the quintessential solution for augmenting the application developer journey in mainframe DevOps due to its innate capacity to comprehend, guide, and enhance code-related tasks. Unlike traditional AI methodologies, which primarily focus on system optimization and anomaly detection, generative AI transcends these boundaries by actively participating in the software development lifecycle. For instance, in code explanation tasks, generative AI not only provides comprehensive insights into code snippets but also automatically integrates explanatory comments, fostering better understanding and collaboration among developers.

Similarly, in code-review scenarios, generative AI offers real-time guidance on adhering to enterprise coding standards and industry best practices, thereby ensuring consistency and compliance across applications. These examples underscore the multifaceted capabilities of generative AI in enhancing developer productivity and facilitating seamless collaboration within the DevOps ecosystem.

Let’s explore five key use cases that demonstrate the efficacy of generative AI in this realm:

1. Explanation: Understand what the code does

Generative AI offers in-depth explanations of code snippets and programs. Leveraging sophisticated natural language processing (NLP) methods, it seamlessly integrates comments into code, improving readability and fostering collaboration among developers. This automated process enhances understanding and streamlines the development workflow, ensuring that developers can grasp the intricacies of the codebase more efficiently, which helps accelerate the development cycle.

2. Review: Receive immediate feedback on code standards

Generative AI plays a pivotal role in code reviews, assisting developers with real-time guidance and remediation suggestions that ensure adherence to predefined standards to promote consistency and compliance across applications. Developers benefit by receiving immediate feedback on their code, which helps them identify and rectify potential issues early in the development process. Consequently, it leads to higher-quality codebases, improved software reliability, and increased efficiency in development workflows.

3. Improve the delivery of your changes

In an era characterized by increasingly complex toolchains, generative AI can significantly streamline the code delivery process. By facilitating quicker root cause isolation and enhancing the resiliency and performance of continuous integration and continuous delivery (CI/CD) toolchains, it expedites mean time to resolution (MTTR). Reduced downtime and faster issue resolution lead to increased productivity and smoother workflows for developers, while businesses experience improved software delivery efficiency, enhanced competitiveness, and greater customer satisfaction.

4. Give every developer a personal assistant

Generative AI functions as an indispensable virtual coding assistant, equipped with extensive knowledge of best practices, design patterns, and syntax. By seamlessly integrating into developer workflows, it provides contextual recommendations and promotes adherence to industry and organizational standards, enhancing developer productivity and code quality. Developers gain real-time guidance, enabling them to make informed decisions and produce high-quality code efficiently . As a result, businesses get accelerated development cycles, reduced error rates, and enhanced software reliability, ultimately leading to improved customer satisfaction and competitive advantage in the market .

5. Efficient testing with minimal viable data

Generative AI revolutionizes the testing paradigm by intelligently analyzing source code and devising optimal testing strategies. By leveraging sophisticated algorithms, it generates test case scripts and tailors datasets to the specific requirements of the application, ensuring thorough coverage while maintaining efficiency. Automation of the testing process saves developers time and effort previously spent creating comprehensive test suites so they can focus more on developing new features and addressing critical issues. For businesses, the adoption of generative AI for test case generation results in improved software quality and reliability . With thorough testing coverage, organizations can mitigate the risk of bugs and performance issues, leading to higher customer satisfaction and reduced maintenance costs in the long run. Additionally, it facilitates faster release cycles, enabling businesses to stay competitive in the market.

Considerations for adoption

When considering the adoption of generative AI in mainframe DevOps, organizations should carefully evaluate several key factors to ensure successful implementation:

• Scalability and compatibility with existing mainframe infrastructure and development processes
• Potential benefits, such as improved code quality, faster development cycles, and enhanced developer collaboration, etc.
• Cost-effectiveness and return on investment, encompassing initial investments in training and infrastructure and the long-term benefits of increased efficiency and productivity
• Alignment with strategic objectives and overall digital transformation initiatives

By carefully weighing these considerations, organizations can make informed decisions about whether and how to incorporate generative AI into their mainframe DevOps workflows.

Conclusion: Generative AI fosters a culture of continuous improvement

In essence, the integration and adoption of generative AI into mainframe DevOps signifies a transformative leap in the developer journey. With enhanced code comprehension and improved testing and adherence to standards, developers are empowered to innovate with unparalleled confidence and agility. This not only accelerates development cycles but also fosters a culture of continuous improvement within organizations.

Embracing generative AI paves the way for enhanced efficiency, reliability, and scalability in mainframe application development. Businesses can expect streamlined processes, reduced time to market, and better software quality. Moreover, the adoption of generative AI sets the stage for future advancements in technology and reinforces the organization’s position at the forefront of innovation in the competitive landscape. Overall, it signifies a strategic investment in harnessing the full potential of AI to drive sustainable growth and success in the digital era.

We are excited to collaborate with IBM as the company unveils its new IBM z16 and LinuxONE 4 single frame and rack mount models available globally on May 17, 2023. Powered by the IBM Telum processor, these new configurations are designed for highly efficient data centers with sustainability in mind. This can help BMC clients make more effective use of their data center space while remaining resilient in the midst of ongoing global uncertainty.

As the IBM z16 single frame and rack mount models bring the strength of accelerated AI to more small and mid-size mainframe shops, organizations increasing their focus on modernization will see exciting new opportunities. BMC Automated Mainframe Intelligence (AMI) solutions are perfectly suited to help them take advantage of these opportunities to grow and build workloads as they modernize and expand on the platform.

BMC AMI DevX application development solutions empower developers with unfettered innovation, helping organizations satisfy customers and grow their customer base, while the AI-enhanced monitoring capabilities and scalability of BMC AMI Ops enables organizations to detect and resolve issues faster, even as workloads increase.

These growing workloads and faster, more agile development, of course, bring a higher volume of data. With BMC AMI Data, organizations can automate and more easily manage this data, while gaining new AI-powered insights. And BMC AMI Security ensures that this business-critical data is not compromised, hardening the mainframe with automated protection, detection, and response to security threats and utilizing the power of AI for enhanced fraud detection.

Addressing today’s changing IT landscape

Every day, clients face challenges in delivering integrated digital services. According to IBM’s recent IBM Transformation Index report, security, managing complex environments, and regulatory compliance were cited as challenges to integrating workloads in a hybrid cloud. In today’s evolving IT landscape, it can be difficult for clients to meet business objectives while adhering to environmental regulations and increasing costs.

The new rack mount option is designed with the same reliability standards as all IBM z16 and LinuxONE systems and is for client-owned data center racks and power distribution units. This footprint is architected to let companies co-locate the latest z16 and LinuxONE 4 technology with distributed infrastructure and opens opportunities to include storage, SAN, and switches in one frame, designed to optimize both data center planning and latency for specific computing projects. Installing these systems in the data center can help create a new class of use cases, including data center design, optimized edge computing, and data sovereignty for regulated industries.

Securing data on a highly available system

According to IBM’s Cost of a Data Breach report, conducted independently by Ponemon Institute, and sponsored, analyzed and published by IBM Security, surveyed organizations with a hybrid cloud model had lower average data breach costs, about $3.8 million, compared to public or private cloud models. IBM z16 and LinuxONE 4 systems help support a secured, available hybrid IT environment critical to customer outcomes for essential industries like healthcare, financial services, government, and insurance.

More sophisticated cyber threats require new standards of protection. IBM z16 and LinuxONE 4 provide high levels of resiliency offering support for mission-critical workloads. These high availability levels help end users maintain access to data from their bank accounts, medical records, and other personal information whenever they need it. IBM z16 and LinuxONE 4 single frame and rack mount systems offer a broad range of security capabilities, including confidential computing, centralized key management, and quantum-safe cryptography.

Optimizing flexibility and sustainability

IBM z16 and LinuxONE 4 single frame models are built to help maximize flexibility and sustainability in data centers. With a new partition-level power monitoring capability and additional environmental metrics, these single frame systems are dedicated to helping clients reach their sustainability goals, reducing data center space and energy consumption. These key advantages distinguish the platforms for sustainability in the data center, especially when consolidating workloads from x86 servers.

As a part of the IBM Ecosystem, BMC is helping companies unlock the value of their infrastructure investments by implementing the tools and technologies designed to help them succeed in a hybrid cloud world. We are excited to be working closely with the IBM Ecosystem to bring new innovations to our clients.

Additional information:

• Read IBM’s press release
• Learn more about BMC
• Learn more about IBM z16 and LinuxONE 4