Summary
Writing code is only one piece of creating good software. Code reviews are an important step in the process of building applications that are maintainable and sustainable. In this episode On Freund shares his thoughts on the myriad purposes that code reviews serve, as well as exploring some of the patterns and anti-patterns that grow up around a seemingly simple process.
Announcements
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- Your host as usual is Tobias Macey and today I’m interviewing On Freund about the intricacies and importance of code reviews
Interview
- Introductions
- How did you get introduced to Python?
- Can you start by giving us your description of what a code review is?
- What is the purpose of the code review?
- At face value a code review appears to be a simple task. What are some of the subtleties that become evident with time and experience?
- What are some of the ways that code reviews can go wrong?
- What are some common anti-patterns that get applied to code reviews?
- What are the elements of code review that are useful to automate?
- What are some of the risks/bad habits that can result from overdoing automated checks/fixes or over-reliance on those tools in code reviews?
- identifying who can/should do a review for a piece of code
- how to use code reviews as a teaching tool for new/junior engineers
- how to use code reviews for avoiding siloed experience/promoting cross-training
- PR templates for capturing relevant context
- What are the most interesting, innovative, or unexpected ways that you have seen code reviews used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while leading and supporting engineering teams?
- What are some resources that you recommend for anyone who wants to learn more about code review strategies and how to use them to scale their teams?
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Picks
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.
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