You're clashing with a team member on code optimization. How do you find common ground?

Open Communication: Initiate a respectful conversation to discuss each other’s perspectives on the optimization issue. Listen Actively: Take the time to understand their approach and reasoning. Acknowledge their points before presenting your own.

When clashing with a team member over code optimization, it's important to focus on open communication. I would start by understanding their perspective and sharing my own reasons for the optimization approach. Finding common ground can come from agreeing on key goals, like performance, readability, or scalability. Testing both solutions and comparing their outcomes can help identify the best path forward. Staying open to compromise and focusing on the shared goal of improving the code helps resolve conflicts and ensures the team moves in the right direction together

Active listening is the key here. Understand their perspective. Ask clarifying questions and understand the reasoning behind their approach. While clever optimizations can save a company thousands of dollars a pre-mature optimization can be a waste of time. Then there's the question of how much would you optimize. Let's consider the following scenario. Let's say you have an application that visualizes data. Your team member proposed to optimize the algorithm as it take too long to load the data. Let's say she wants to rewrite a search algorithm. However, it will delay the deadline. In this case have an open conversation, maybe a simple trick to memoize algorithm would be enough. Acknowledge their points before presenting your ideas.

Some optimizations in code are actually more than just optimizations: refactoring code may improve code clarity. Such optimizations are almost always welcomed as soon as they require reasonable amount of time to implement given the current project priorities. Other types of optimizations, like replacing some algorithm in your project codebase with a completely different one, may result in more complicated code, and in this case should be discussed with the team prior to implementation. When optimizing code computationally, it is helpful to first write a decent number of tests that the old code passes and use TDD (test-driven) approach to implement the new code even if in general TDD is not employed in the organization on a large scale.