Subtitle: Exploring the Challenges of Benchmarking and Network Building
In the world of software development, optimizing build times is crucial for maintaining productivity and efficiency. Recently, an article sparked a discussion about different methods of analyzing developer build times and the challenges that come with it. The article highlighted the importance of accurate data collection and analysis while acknowledging the potential biases in sample sets.
Variety of Data Collection and Analysis Methods
The article praised the comprehensive approach taken to collect and analyze data on developer build times. The author suggested that it would have been easier and more accurate to directly compare laptops by running timed compilations on the exact same scenarios. This method would eliminate potential biases caused by using different laptop models.
Additionally, the author mentioned the idea of creating a script to apply incremental git commits and measure timed incremental builds. This approach would provide a representation of incremental build times for actual code.
Biases in Sampling and Representative Data
The author raised concerns about the sampling process and biases within the data. They pointed out that newer employees are more likely to have newer laptops, while the oldest employees may still be using older laptop models. The tasks assigned to employees with different laptops could also differ, leading to variations in build times.
While acknowledging the benefits of collecting company-wide statistics, the author emphasized the need to consider potential biases in sample sets. They suggested starting with a simpler method of benchmarking recent commits on each laptop before investing time and effort into company-wide data collection.
Considering Network Building as an Alternative
A reader of the article proposed an alternative to simply upgrading laptops: network building. They described their experience with a large codebase and how the use of network builds significantly improved build times. In their case, the network build provided a 15x speed increase compared to a laptop upgrade, making it a viable option.
However, the author of the original article argued that network building might not be suitable for all companies. They mentioned the need for a dedicated infrastructure team and significant investment in adopting new tools and technologies like bazel. Additionally, network building may not offer the same flexibility and convenience as working directly on laptops, such as the ability to work offline.
The Role of Data Scientists and Visualization Techniques
The discussion touched on the role of data scientists in analyzing developer build times. While the author of the article appreciated the graphs and analysis presented, they pointed out missed opportunities, such as using linear regression or more advanced statistical tests like the Jonckheere-Terpstra test. They also suggested supplementing histograms with means and error bars or using cumulative distribution functions (CDFs) for comparing distributions.
In response, some participants noted that expecting every software engineer to have expertise in advanced statistical analysis or visualization techniques is unrealistic. They argued that most engineers rely on the tools and methods provided by analytics and performance analysis software suites.
The discussion surrounding the analysis of developer build times highlights the importance of collecting accurate data and considering potential biases. While different methods of analyzing build times present their own advantages and challenges, the best approach may vary depending on the company’s specific needs and resources.
Whether it’s upgrading laptops or implementing network building, the goal is to improve productivity and provide developers with the best possible experience. Ensuring an efficient and streamlined build process can greatly impact a company’s success in the fast-paced world of software development.
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Author Eliza Ng