By Christopher W. H. Davis
Venture monitoring structures, attempt and construct instruments, resource regulate, non-stop integration, and different integrated components of the software program improvement lifecycle generate a wealth of information that may be used to trace and enhance the standard and function of goods, procedures, and groups. even though the iterative nature of Agile improvement is ideal for data-driven non-stop development, the gathering, research, and alertness of significant metrics usually fades in desire of subjective measures that supply much less perception into the true demanding situations of creating larger software.
Agile Metrics in motion: Measuring and embellishing the functionality of Agile groups is a pragmatic publication that indicates find out how to take the information already being generated to make groups, approaches, and items greater. It issues out which metrics to take advantage of to objectively degree functionality and what info particularly counts, in addition to the place to discover it, the best way to get it, and the way to research it. The publication additionally indicates how all group contributors can submit their very own metrics via dashboards and radiators, taking cost of speaking functionality and person responsibility. alongside the way in which, it bargains sensible information research concepts, together with a number of rising huge information practices.
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Additional info for Agile Metrics in Action: Measuring and Enhancing the Performance of Agile Teams
1. org/ overview/) for production log analysis. 2 A problem arises In one of the acquisitions, the team got a robust piece of e-commerce software that complemented a lot of their current site’s functionality. com> New Relic and ELK 22 CHAPTER 2 Observing a live project Queries for warnings and errors in the application logs. A small number of issues is expected in production. Huge jump in the number of errors and warnings is not good. 2 Kibana visualizing log analysis for the case-study project.
According to the data they could see the following: ■ ■ ■ Before the refactor the development team was averaging 12 points/developer/ sprint (pds). The refactor team was still averaging 12 pds while changing a lot more code. The new-feature team was down to 4 pds. These results led them to the following conclusions: ■ ■ Given their productivity was cut to one-third, if they could stop working for two sprints and get back to their average productivity on the third sprint, they would get the same amount of work done as if they worked at one-third productivity for three sprints.
There are easy ways to use out-of-the-box technology to obtain metrics very quickly. In appendices A and B we outline open source tools you can use to get metrics from your existing systems with little effort. The key is using the data you have and simply making metrics the byproduct of your work. Summary This chapter showed you where to find data to measure your team and your process and an overview of what to do with it. At this point you’ve learned: ■ ■ ■ ■ ■ ■ Measuring agile development is not straightforward.