探究SOLID设计原则对机器学习代码理解的影响
Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding
AI总结：
本研究通过受控实验，发现采用SOLID设计原则能显著提升机器学习代码的理解性，建议在数据科学社区推广这些原则以增强代码可维护性。
摘要：
[背景] 长期以来，应用设计原则被认为有益于传统软件项目的理解性和可维护性。这些益处可能同样适用于机器学习（ML）项目，此类项目涉及对数据、模型和算法的迭代实验。然而，ML组件通常由具有不同教育背景的数据科学家开发，可能导致代码不符合软件设计最佳实践。[目标] 为更好地理解这一现象，我们研究了SOLID设计原则对ML代码理解的影响。[方法] 我们进行了一项受控实验，包含三项独立试验，涉及100名数据科学家。我们对未使用SOLID原则的真实工业ML代码进行了重构。在每项试验中，一组被展示原始ML代码，而另一组被展示融入SOLID原则的ML代码。两组参与者均被要求分析代码并填写一份包含开放式和封闭式问题的问卷，以评估其理解程度。[结果] 研究结果提供了统计上显著的证据，表明采用SOLID设计原则可以改善ML项目领域内的代码理解。[结论] 我们提出，软件工程设计原则应在数据科学社区中传播，并考虑用于增强ML代码的可维护性。
英文摘要：
[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scientists with diverse educational backgrounds, potentially resulting in code that doesn't adhere to software design best practices. [Goal] In order to better understand this phenomenon, we investigated the impact of the SOLID design principles on ML code understanding. [Method] We conducted a controlled experiment with three independent trials involving 100 data scientists. We restructured real industrial ML code that did not use SOLID principles. Within each trial, one group was presented with the original ML code, while the other was presented with ML code incorporating SOLID principles. Participants of both groups were asked to analyze the code and fill out a questionnaire that included both open-ended and closed-ended questions on their understanding. [Results] The study results provide statistically significant evidence that the adoption of the SOLID design principles can improve code understanding within the realm of ML projects. [Conclusion] We put forward that software engineering design principles should be spread within the data science community and considered for enhancing the maintainability of ML code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
