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5 months ago AMI DevX Abend-Aid + zAdviser use case: Podcast - A day in the life of the Abend hunter
TL;DR The toil that mainframe developers go through to trace down the behaviors of Abends is extra ordinary. When manager consider the high cost of legacy developer skill sets, everyone agrees that there's got to be a better way of excising the gremlins from your mainframe application code. And if these Abends escape into production, as they can and do, developers don't have visibility into the entire SDLC, and they do things manually, which increases the odds that something's going to go arwy. Leverage the power of machine learning and artificial intelligence to reduce their mean time to remediate by leveraging code commits, traces, and visualization tools to discover and reproduce the trouble quickly before they escape.
Speakers This podcast will feature Mark Shettenhelm and Spencer Hallman Moved this idea to Project #30
AMI DevX Abend-Aid + zAdviser use case: Blog - MTTR
TL;DR Abend-Aid + zAdviser can gather up all of the escaped Abends and analyze how frequently they occur. With these trended insights visualized through zAdviser, developers can pinpoint the patterns where the trouble begins and trace down what code commits created the problems.
This is a reimagining of a feature that has been part of Abend-AID for a long time.
The plan: Build a halo of thought leadership around this use case of reducing MTTR.
The pain: Tracing down Abends is a pain for the mainframe developer because they have to check in the green screen to track down the Abend, but they can't reproduce the problem because it is a dynamic event. NO one wants to waste time searching for something.
The answer: using pulling in code commits from Code Pipeline and analyzing them against Abends in zAdviser gives developers the insights effortlessly, by using zAdviser's machine learning capabilities to understand the patterns of dynamic and static events, it learns what good looks like. zAdviser then visualizes the insights and deploys AI-led recommendations to alert developers to areas for improvement.