 Hi, I'm Ty Davis today. We're going to talk get lab value stream management and the productivity insights that are available With those the developer insights security insights operation insights those those are giving us views at either a specific project level or you know a step above if we want to take a view at that team Productivity insights this allows us to take More holistic view of specific metrics KPIs and our software delivery life cycle Product analytics that they're what this is doing is it's going to track philosophy. So for many companies The development cycle, this is a black box and it's getting an estimate of how long on average, you know It takes to deliver features Which is normally an enormous endeavor With the value stream analytics that we first started with that focuses on that entire software development life cycle of these productivity analytics They're going to provide a way for engineering management to drill down Again in a systematic way to uncover patterns and causes for the success or failure all from a Software development life cycle a platform that provides this kind of single source of truth Productivity can slow down for many reasons ranging from to creating code based to maybe growing quickly or quickly growing teams So what we can do with productivity is we can Visualize something like merger quest lifetime statistics We can use a histogram that shows the distribution of time elapsed in between creating and merging merger quest Maybe I am an engineering manager and I'm wondering why merger quest are taking longer than 10 days here. So if I click on 10 days You know our sprints, maybe they're two weeks and 10 days is pushing it closest You know, we look to stay on track with our velocity estimates. So what is holding up an MR from? the last time a Commit was made and in between that time to merging that to master branch So I have someone that's made a commit and it's just sitting in queue Let's look at this one that has a hundred and one hours You know want to see This one's been merged, but this one had a long queue time So what what was you know that problem that made it in queue from the time that that person committed their last Commit to when it was actually merged and the traceability aspect of going into that that merge request and seeing all the different Actions that took place and you get a Time stamp of everything that happened. So this is able to Me as a engineer manager to understand why my team may be having a A a bottleneck in pushing out something from that last commit to actually getting it to production With issue analytics, this helps gauge our approach to if we look at like an agile project management approach maybe how Customer feedback impacts our backlogs Issue analytics is just a bar graph, which is going to illustrate the number of issues created in specific time frames Right now what I'm looking at I we have labels which are a very big part of get lab and Sorry, not labels labels are but in this particular case a milestone Which is a time box period milestones inside of get lab is where you're going to define your sprints or your releases And this particular case We're looking at get labs 13.0 release That's our upcoming release and we can see the issues that were opened up against that release we can see obviously an increase in the in the last few months as Issues are being added or they're being moved from Previous releases that that maybe didn't make that that previous or earlier release and has moved on to 13.0 so code of view The addressing software lifecycle products and this could be done Via code of view dashboard and this provides a list of open merge requests that may be engineering needs to address from a Again a manager or a director of you. I can see open Merger quest I can see how much review time I can see the comments around that to commit to made around it How many line changes so this review time is 185 days. Why why is this taking so long for an engineer? To get done I can dive into that and then take again a look at the different reasons why it may have not been completed Pipeline failure dive into that, but this view is going to give me You know a is designed for development team leaders and others who want to understand that broad code review dynamics Identify patterns to help explain them So, you know, you can expose your team's unique challenges with this code of view Again identifying improvements that maybe like substantially That may substantially help you accelerate your dev life cycle as As the count of issues or merger quests and epics they grow within get lab It's it's more it becomes more and more challenging to keep track of those items Especially as an organization, maybe it's already larger. It continues to grow in This case for us from a few hundred people to thousands This is where something such as labels comes into get lab and With labels they help you Organize and tag work so you can track and find the work items that you're most interested in Anything that can be labeled can be tracked in get lab. So this insights view is a configuration of different labels and it's been customized for get lab org to have view into in this particular case priorities of issues that came in and It's gonna show me a aggregated view of those different priorities again, I can manipulate the time frame There's customization that's been done around these insights anything from We look at bug classification. So if I want to see and hopefully this Doesn't take too long to load, but if I want to see the bugs that have came in over For the the organization as a whole I can see again the priorities around open bugs the amount that are That are directly involved to the priority and then Address those if this is something a specific dashboard that we need to look at so we end in transient failures Thanks for watching our video on productivity insights as part of get labs value stream management Be sure to check out more videos on our YouTube channel