Beware the Share

本文通过作者初入职场的经历,阐述了在不同上下文环境中代码复用的利弊,强调了在进行代码复用时需要考虑的背景因素。文章深入探讨了代码复用在提高软件质量和效率方面的潜在价值,同时也指出了过度依赖代码复用可能导致的维护成本增加和系统复杂度提升的问题。通过实例分析,作者呼吁开发者在应用代码复用技术时要谨慎考量其适用场景,以避免潜在的风险。

It was my first project at the company. I'd just finished my degree and was anxious to prove myself, staying late every day going through the existing code. As I worked through my first feature I took extra care to put in place everything I had learned — commenting, logging, pulling out shared code into libraries where possible, the works. The code review that I had felt so ready for came as a rude awakening — reuse was frowned upon!

How could this be? All through college reuse was held up as the epitome of quality software engineering. All the articles I had read, the textbooks, the seasoned software professionals who taught me. Was it all wrong?

It turns out that I was missing something critical.

Context.

The fact that two wildly different parts of the system performed some logic in the same way meant less than I thought. Up until I had pulled out those libraries of shared code, these parts were not dependent on each other. Each could evolve independently. Each could change its logic to suit the needs of the system's changing business environment. Those four lines of similar code were accidental — a temporal anomaly, a coincidence. That is, until I came along.

The libraries of shared code I created tied the shoelaces of each foot to each other. Steps by one business domain could not be made without first synchronizing with the other. Maintenance costs in those independent functions used to be negligible, but the common library required an order of magnitude more testing.

While I'd decreased the absolute number of lines of code in the system, I had increased the number of dependencies. The context of these dependencies is critical — had they been localized, it may have been justified and had some positive value. When these dependencies aren't held in check, their tendrils entangle the larger concerns of the system even though the code itself looks just fine.

These mistakes are insidious in that, at their core, they sound like a good idea. When applied in the right context, these techniques are valuable. In the wrong context, they increase cost rather than value. When coming into an existing code base with no knowledge of the context where the various parts will be used, I'm much more careful these days about what is shared.

Beware the share. Check your context. Only then, proceed.

By Udi Dahan


This work is licensed under a Creative Commons Attribution 3

标题基于Python的汽车之家网站舆情分析系统研究AI更换标题第1章引言阐述汽车之家网站舆情分析的研究背景、意义、国内外研究现状、论文方法及创新点。1.1研究背景与意义说明汽车之家网站舆情分析对汽车行业及消费者的重要性。1.2国内外研究现状概述国内外在汽车舆情分析领域的研究进展与成果。1.3论文方法及创新点介绍本文采用的研究方法及相较于前人的创新之处。第2章相关理论总结和评述舆情分析、Python编程及网络爬虫相关理论。2.1舆情分析理论阐述舆情分析的基本概念、流程及关键技术。2.2Python编程基础介绍Python语言特点及其在数据分析中的应用。2.3网络爬虫技术说明网络爬虫的原理及在舆情数据收集中的应用。第3章系统设计详细描述基于Python的汽车之家网站舆情分析系统的设计方案。3.1系统架构设计给出系统的整体架构,包括数据收集、处理、分析及展示模块。3.2数据收集模块设计介绍如何利用网络爬虫技术收集汽车之家网站的舆情数据。3.3数据处理与分析模块设计阐述数据处理流程及舆情分析算法的选择与实现。第4章系统实现与测试介绍系统的实现过程及测试方法,确保系统稳定可靠。4.1系统实现环境列出系统实现所需的软件、硬件环境及开发工具。4.2系统实现过程详细描述系统各模块的实现步骤及代码实现细节。4.3系统测试方法介绍系统测试的方法、测试用例及测试结果分析。第5章研究结果与分析呈现系统运行结果,分析舆情数据,提出见解。5.1舆情数据可视化展示通过图表等形式展示舆情数据的分布、趋势等特征。5.2舆情分析结果解读对舆情分析结果进行解读,提出对汽车行业的见解。5.3对比方法分析将本系统与其他舆情分析系统进行对比,分析优劣。第6章结论与展望总结研究成果,提出未来研究方向。6.1研究结论概括本文的主要研究成果及对汽车之家网站舆情分析的贡献。6.2展望指出系统存在的不足及未来改进方向,展望舆情
【磁场】扩展卡尔曼滤波器用于利用高斯过程回归进行磁场SLAM研究(Matlab代码实现)内容概要:本文介绍了利用扩展卡尔曼滤波器(EKF)结合高斯过程回归(GPR)进行磁场辅助的SLAM(同步定位与地图构建)研究,并提供了完整的Matlab代码实现。该方法通过高斯过程回归对磁场空间进行建模,有效捕捉磁场分布的非线性特征,同时利用扩展卡尔曼滤波器融合传感器数据,实现移动机器人在复杂环境中的精确定位与地图构建。研究重点在于提升室内等无GPS环境下定位系统的精度与鲁棒性,尤其适用于磁场特征明显的场景。文中详细阐述了算法原理、数学模型构建、状态估计流程及仿真实验设计。; 适合人群:具备一定Matlab编程基础,熟悉机器人感知、导航或状态估计相关理论的研究生、科研人员及从事SLAM算法开发的工程师。; 使用场景及目标:①应用于室内机器人、AGV等在缺乏GPS信号环境下的高精度定位与地图构建;②为磁场SLAM系统的设计与优化提供算法参考和技术验证平台;③帮助研究人员深入理解EKF与GPR在非线性系统中的融合机制及实际应用方法。; 阅读建议:建议读者结合Matlab代码逐模块分析算法实现细节,重点关注高斯过程回归的训练与预测过程以及EKF的状态更新逻辑,可通过替换实际磁场数据进行实验验证,进一步拓展至多源传感器融合场景。
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