GitPrime Blog

I think the title of this post is great, but the body doesn't quite deliver. The best content I've found on this subject is this book: https://www.amazon.com/gp/product/0932633366 It talks about the different between informative and motivational metrics - the former being just for awareness while the latter being intended as explicit targets for employees. While it's easy to bleed the former into the latter, there's nothing inherently wrong with measuring game-able statistics as long as the incentive to muck with them is minimized. Easier said than done, but not impossible. The article is pretty dismissive of story counting, which is a semi-popular approach to estimation: https://martinfowler.com/bliki/StoryCounting.html (Anecdotally, we had someone look at our teams' previous sprints and found that # stories / sprint was more consistent than points per sprint over the time frame they reviewed. YMMV.) The focus on velocity-related metrics seems smart - Git Prime did some analysis on this and found that frequency of commits was a useful measure: https://blog.gitprime.com/prolific-engineers-take-small-bites-patterns… I don't see how these are any less game-able than klocs though.

Neither in the article or in the referenced article/blog post linked to "Impact" is impact defined. https://blog.gitprime.com/impact-a-better-way-to-measure-codebase-chan… What I found is: Impact takes the following into account: The amount of code in the change What percentage of the work is edits to old code The surface area of the change (think ‘number of edit locations’) The number of files affected The severity of changes when old code is modified How this change compares to others from the project history No exact or detailed formula for "impact" is given. "takes the following into account" is extremely vague as it could indicate any relationship. Is impact higher with more files changed in the commit? Why would it be better if more files are changed? Is it lower? Why would it be better if fewer files are changed? Is it some totally unobvious non-linear function such as a trigonometric sine? Based on the vague description above, nothing in "impact" is directly related to the actual end user/paying customer experience or a reasonable proxy such as systematic end user testing by a QA team. This lack of a direct relationship to the desired end result is the same problem that lines of code (loc or LoC) and many other metrics of software engineer output have. The "impact" metric, whatever it precisely is, looks suspiciously like it would naturally be positively correlated with a large number of commits/high frequency of…

A Python script to compute "true" code churn of a Git repository. Especially useful for software teams. Code churn has several definitions, the one that to me provides the most value as a metric is: "Code churn is when an engineer rewrites their own code in a short period of time." Reference: https://blog.gitprime.com/why-code-churn-matters/ Solutions that I've found online looked at changes to files irrespective whether these are new changes or edits to existing files. Hence this solution that segments code edits (churn) with new code changes (contribution).

They load something called smooth scroll, which is for creating mac like smooth scrolling on other systems. https://blog.gitprime.com/wp-content/themes/Divi/js/smoothscroll.js?ve… In chrome you can open the network tab, filter on JS, find the smoothscroll request and right click and block it :D

I encourage you to keep writing, but it seems you need more research into developer patterns if you want to contribute to an already highly discussed topic. For instance, last month this article[1] gave some seriously good insights to developer checkin frequency based on hard data. It provides a lot more value than the anecdotal, muddy definitions of over/under/engineer. [1] - https://blog.gitprime.com/check-in-frequency-and-codebase-impact-the-s…

They go into slightly more detail on factors that influence the impact rating in a linked post. It still doesn't explain what formula or weightings they use. https://blog.gitprime.com/impact-a-better-way-to-measure-codebase-chan…

@shandor — yeah, there's some secret sauce happening in that impact metric, but this post breaks it down so you get a sense of how it's calculated and how teams are using it: https://blog.gitprime.com/impact-a-better-way-to-measure-codebase-chan…