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In software engineering, there is a direct correlation between team performance and building robust, stable applications. The data community aims to adopt the rigorous engineering principles commonly used in software development into their own practices, which includes systematic approaches to design, development, testing, and maintenance. This requires carefully combining applications and metrics to provide complete awareness, accuracy, and control. It means evaluating all aspects of a teams performance, with a focus on continuous improvement, and it applies just as much to mainframe as it does to distributed and cloud environmentsmaybe more.

This is achieved through practices like infrastructure as code (IaC) for deployments, automated testing, application observability, and complete application lifecycle ownership. Through years of research, the DevOps Research and Assessment (DORA) team has identified four key metrics that indicate the performance of a software development team:

These metrics provide a quantitative way to measure the effectiveness and efficiency of DevOps practices. Although much of the focus around analysis of DevOps is on distributed and cloud technologies, the mainframe still maintains a unique and powerful position, and it can use the DORA 4 metrics to further its reputation as the engine of commerce.

This blog post discusses how BMC Software addedAWS Generative AIcapabilities to its productBMC AMI zAdviser Enterprise. The zAdviser usesAmazon Bedrockto provide summarization, analysis, and recommendations for improvement based on the DORA metrics data.

Tracking DORA 4 metrics means putting the numbers together and placing them on a dashboard. However, measuring productivity is essentially measuring the performance of individuals, which can make them feel scrutinized. This situation might necessitate a shift in organizational culture to focus on collective achievements and emphasize that automation tools enhance the developer experience.

Its also vital to avoid focusing on irrelevant metrics or excessively tracking data. The essence of DORA metrics is to distill information into a core set of key performance indicators (KPIs) for evaluation. Mean time to restore (MTTR) is often the simplest KPI to trackmost organizations use tools like BMC Helix ITSM or others that record events and issue tracking.

Capturing lead time for changes and change failure rate can be more challenging, especially on mainframes. Lead time for changes and change failure rate KPIs aggregate data from code commits, log files, and automated test results. Using a Git-based SCM pulls these insight together seamlessly. Mainframe teams using BMCs Git-based DevOps platform, AMI DevX ,can collect this data as easily as distributed teams can.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

BMC AMI zAdviser Enterprise provides a wide range of DevOps KPIs to optimize mainframe development and enable teams to proactvely identify and resolve issues. Using machine learning, AMI zAdviser monitors mainframe build, test and deploy functions across DevOps tool chains and then offers AI-led recommendations for continuous improvement. In addition to capturing and reporting on development KPIs, zAdviser captures data on how the BMC DevX products are adopted and used. This includes the number of programs that were debugged, the outcome of testing efforts using the DevX testing tools, and many other data points. These additional data points can provide deeper insight into the development KPIs, including the DORA metrics, and may be used in future generative AI efforts with Amazon Bedrock.

The following architecture diagram shows the final implementation of zAdviser Enterprise utilizing generative AI to provide summarization, analysis, and recommendations for improvement based on the DORA metrics KPI data.

The solution workflow includes the following steps:

The following screenshot shows the LLM summarization of DORA metrics generated using Amazon Bedrock and sent as an email to the customer, with a PDF attachment that contains the DORA metrics KPI dashboard report by zAdviser.

In this solution, you dont need to worry about your data being exposed on the internet when sent to an AI client. The API call to Amazon Bedrock doesnt contain any personally identifiable information (PII) or any data that could identify a customer. The only data transmitted consists of numerical values in the form of the DORA metric KPIs and instructions for the generative AIs operations. Importantly, the generative AI client does not retain, learn from, or cache this data.

The zAdviser engineering team was successful in rapidly implementing this feature within a short time span. The rapid progress was facilitated by zAdvisers substantial investment in AWS services and, importantly, the ease of using Amazon Bedrock via API calls. This underscores the transformative power of generative AI technology embodied in the Amazon Bedrock API. This API, equipped with the industry-specific knowledge repository zAdviser Enterprise and customized with continuously collected organization-specific DevOps metrics, demonstrates the potential of AI in this field.

Generative AI has the potential to lower the barrier to entry to build AI-driven organizations. Large language models (LLMs) in particular can bring tremendous value to enterprises seeking to explore and use unstructured data. Beyond chatbots, LLMs can be used in a variety of tasks, such as classification, editing, and summarization.

This post discussed the transformational impact of generative AI technology in the form of Amazon Bedrock APIs equipped with the industry-specific knowledge that BMC zAdviser possesses, tailored with organization-specific DevOps metrics collected on an ongoing basis.

Check out the BMC website to learn more and set up a demo.

Sunil Bemarkar is a Sr. Partner Solutions Architect at Amazon Web Services. He works withvarious Independent Software Vendors (ISVs)and Strategic customers across industries to accelerate their digital transformation journeyand cloud adoption.

Vij Balakrishna is a Senior Partner Development manager at Amazon Web Services. She helps independent software vendors (ISVs) across industries to accelerate their digital transformation journey.

Spencer Hallman is the Lead Product Manager for the BMC AMI zAdviser Enterprise. Previously, he was the Product Manager for BMC AMI Strobe and BMC AMI Ops Automation for Batch Thruput. Prior to Product Management, Spencer was the Subject Matter Expert for Mainframe Performance. His diverse experience over the years has also included programming on multiple platforms and languages as well as working in the Operations Research field. He has a Master of Business Administration with a concentration in Operations Research from Temple University and a Bachelor of Science in Computer Science from the University of Vermont. He lives in Devon, PA and when hes not attending virtual meetings, enjoys walking his dogs, riding his bike and spending time with his family.

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Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock | Amazon Web Services - AWS Blog