I think you can get a long way with "automated code reviews" - basically, linting and static analysis. There are various ones out there for your language - disclaimer: I wrote one for Python called https://landscape.io
They can take a little while to get set up exactly how you like them, but once you do, run them on your CI server after every commit and it'll output the warnings about style violations and so on. That's basically what you're after. It's not a replacement for a manual code review but it's certainly closer to what you want than nothing!
As other have said though, this only works if you and your team treat code quality as important and actually take the time to understand the output of the analysis tools. That's why I like the CI approach, so that you can see if the number of warnings is going up or down over time and react accordingly. That metric is a useful one to keep an eye on to get a view on the quality of your code, and if it gets too high you can schedule some time.
I just spent the better part of a month cleaning up some CI issues. By the time I go to bed tonight, I hope to have a CI process completed for two platform implementations of an application that's been under development for some time. Part of the process is static analysis and linting.
I've seen the static analysis and lint results already. Not pretty, but we can clear most of it up as we develop new features.
Definitely a process that anything beyond a trivial app needs in place.
this is great - I love coding in Python and would definitely use this in the future. Currently we're doing everything in Node.JS though - do you know of any tools for that ? We do use Jslint etc - but landscape certainly seems to go a step further..
They can take a little while to get set up exactly how you like them, but once you do, run them on your CI server after every commit and it'll output the warnings about style violations and so on. That's basically what you're after. It's not a replacement for a manual code review but it's certainly closer to what you want than nothing!
As other have said though, this only works if you and your team treat code quality as important and actually take the time to understand the output of the analysis tools. That's why I like the CI approach, so that you can see if the number of warnings is going up or down over time and react accordingly. That metric is a useful one to keep an eye on to get a view on the quality of your code, and if it gets too high you can schedule some time.
(PS if you are interested in learning more about "Automatic Code Reviews" for Python, here's a talk I did at EuroPython14: http://carlcrowder.com/pages/europython-2014-automatic-code-... )