This is the best summary I could come up with:
Over the decades, engineering management has undoubtedly become more agile and data-driven, with automated data gathering improving performance.
It can automatically set goals based on real-time data, generate recommendations for improving teams’ performance, and process far more information than was possible before.
Even the most capable engineering leaders have some blind spots when it comes to reviewing performance in certain areas, and may miss concerning behaviors or causal factors.
Typically, managers will manually put together reports at the end of the month or quarter, but often that gives a superficial analysis that can easily conceal hidden or incipient problems.
Or, it may find that longer review times are simply delaying the development process without any significant reduction in churn.
By analyzing multiple metrics simultaneously, AI can help identify patterns and correlations that might not be immediately apparent to managers, enabling organizations to make more informed decisions to optimize their software development processes.
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thesmokingman@programming.dev 1 year ago
This sounds like a bunch of drivel.
This is not something that the numbers tell you. This is something an understanding of the code reviews tells you. The author runs a metrics platform so he’s pushing this hype train that’s going nowhere. Blind faith in metrics without context, ie all an AI can generate, leads to great decisions like the Nova.
meco03211@lemmy.world 1 year ago
My experience has been management is too stupid to properly employ metrics. If you let them pick, they pick the easiest ones. If they don’t get a choice, they just try to cheese the numbers to the detriment of the company. If you present options that make sense, they don’t understand them.
Maybe I’ve just worked for dumb companies, but they sucked hard with respect to metrics.
jungle@lemmy.world 1 year ago
As an engineering manager who’s tasked with manually gathering a whole lot of metrics in the hopes that they will help improve “something”, what would you say are the metrics that make the most sense?
As a former coder, I would say PR size (the smaller the better), automated test coverage, and number of prod bugs, toil level (unplanned work, dealing with tools and environment issues) but also things like knowledge of the code base, level of collaboration, ownership of the process, org-wide collaboration and learning… Much of which is hard to measure but can be set as goals regardless.