.NET Tips and Tricks

Blog archive

Proving You're Making a Difference with Code Metrics

I never get my code right the first time. And, even after my code passes all its tests, it's still not right. That's because I will have learned a lot about the problem when writing my code (wouldn't it be awful if that didn't happen?). But, unfortunately, much of my code reflects decisions made in an early, more ignorant stage of this learning process. As a result, I typically want to take some time, after the code passes its tests, to rewrite my code and make it "better."

The problem is that my clients need some proof that this rewrite is time well spent. One way to do that is to use Visual Studio's Analyze | Calculate Code Metrics menu choice to generate some hard numbers that show how the code is getting "better."

But, as I tell people all the time, no metric makes sense by itself: You need to compare your code's current numbers to what you had before to see if things are getting (in some sense of the word) "better." What you want to do is save your original numbers so you can compare them to your later, "better" numbers.

You have two ways to do this. One way is, in the Code Metric Results window, just select the metrics you're interested in, right-click on them and select Copy. Now you can paste these metrics into any place you want to keep them -- Excel would be a good choice. Of course, if you're doing that, why not just pick the Open List in Excel option on the Code Metric Results' toolbar? Now you can save those results in a workbook for later reference.

Heck, now that you've got those number in Excel, you can create a graph from them. My clients love graphs.

Posted by Peter Vogel on 03/13/2019


comments powered by Disqus

Featured

  • VS Code 1.127 Further Integrates Advanced Browser-AI Tech

    Microsoft's July 1 Visual Studio Code update continues a recent push to make the editor's integrated browser a more capable development surface -- and a more useful tool for AI agents.

  • Support Vector Regression with SGD Training Using C#

    Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

  • New GitHub Switch Limits Repo Issue Creation to Collaborators Only

    After publicly touting pull request limits as a way to cut maintainer noise, GitHub is taking the same idea further with a new setting that lets repository admins restrict issue creation to collaborators only.

  • Uno Platform Helps Ship First Stable SkiaSharp 4.0 Release for 2D .NET Graphics

    SkiaSharp 4.148.0 is the first stable v4 release, bringing a newer Skia engine, API cleanup, performance work and a Microsoft-Uno co-maintenance model.

Subscribe on YouTube