Code quality: measuring the business impact of unhealthy code
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Re: Code quality: measuring the business impact of unhealthy code
#2@john-shaffer if this is your tool can you provide a link to how the various metrics are computed?
edit: FAQ doesn't include the above. It does say the tool requires write access to GitHub repos it analyses.
Re: Code quality: measuring the business impact of unhealthy code
#3Re: Code quality: measuring the business impact of unhealthy code
#4Re: Code quality: measuring the business impact of unhealthy code
#5Re: Code quality: measuring the business impact of unhealthy code
#6Re: Code quality: measuring the business impact of unhealthy code
#7Nice to read – this sheds a new light on to why and how to tackle technical debt.
I wish that I had this type of data myself back in the day. Would have been so much easier to push back on wishful deadlines.
Re: Code quality: measuring the business impact of unhealthy code
#8Re: Code quality: measuring the business impact of unhealthy code
#9This appears to be selling a static analysis tool that rates code on a 1 to 10 scale on a few axes where the assessment criteria are opaque. So on the face of it that's not something developers are going to like. @john-shaffer if this is your tool can you provide a link to how the various metrics are computed? edit: FAQ doesn't include the above. It does say the tool requires write access to GitHub repos it analyses.
Here's a link to some docs about how code health is calculated: https://codescene.io/docs/guides/technical/code-health.html
In simple terms we try to measure cognitive complexity of source code.
A more in-depth description of one of the factors: https://codescene.com/blog/bumpy-road-code-complexity-in-con...
Re: Code quality: measuring the business impact of unhealthy code
#10Good read. I would especially want to know more about the unpredictability of the red code/unhealthy code. The difference between healthy code and red code regarding implementation time is astonishing.
From the paper: "Could it be that changes done by the main author are faster and more predictable than corresponding changes made by a minor contributor? We suspect that author experience could be a factor that impacts the predictability of changes to low quality code."
If that turns out to be the case, then it would highlight organizational dimensions of low quality code: key personnel dependencies and on-boarding challenges.