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Adrift at sea, a GPS device will report your precise latitude and longitude, but if you don't know what those numbers mean, you're just as lost as before. Similarly, there are many tools that offer a wide variety of metrics about your code, but other than making you feel good, what are you supposed to do with this knowledge? Let's answer that question by exploring what the numbers mean, how static code analysis can add value to your development process, and how it can help us chart the unexplored seas of legacy code. Help us caption & translate this video! http://amara.org/v/FGbI/
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The video titled "You Can't Miss What You Can't Measure" presented by Kerri Miller at the Ruby on Ales 2013 conference discusses the significant role that code metrics play in software development and code maintenance. Kerri draws an analogy between navigating unfamiliar territory without proper tools and managing legacy code without understanding its inherent metrics. Key points discussed throughout the video include: - **Importance of Understanding Metrics**: Just as GPS coordinates need context to be meaningful, various coding metrics must be interpreted to be useful in reflecting the health and clarity of the codebase. - **Static Code Analysis**: Kerri emphasizes that static code analysis provides valuable insights, acting as a compass for developers navigating complex legacy systems. Metrics like code complexity, churn rate, and coverage are vital for understanding code quality. - **Types of Metrics**: The speaker introduces different metrics used in code analysis: - **Code Coverage**: Indicates the percentage of code tested by automated tests, but emphasizes that 100% coverage doesn’t guarantee quality. - **Lines of Code**: Outdated metric previously used to measure productivity, which Kerri criticizes. - **Churn**: Tracks how often code files are modified, signifying areas with potential instability. - **Complexity Metrics**: Various forms of complexity metrics like Flog and cyclomatic complexity help identify hard-to-read and maintain pieces of code that may need refactoring. - **Defining Good Code**: Highlights the subjective nature of what constitutes 'good code' and acknowledges that each team should define their own acceptable metrics instead of blindly chasing arbitrary thresholds. - **Tools and Practices**: Kerri mentions several tools utilized in Ruby development, including Code Climate, Metric Fu, and Flay to aid in guiding teams toward better code practices. - **Visualization and Team Collaboration**: Discusses using visual tools to map code structure, which can aid in onboarding new developers and facilitate discussions about code improvements. In conclusion, the primary takeaway from this presentation is the necessity of understanding and leveraging code metrics not just for awareness but as tools to foster better coding practices and enhance collaboration within development teams. Metrics should drive conversations and reflections among developers rather than being used as arbitrary judgments of their coding capabilities.
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