Some lesser-known truths about programming

本文揭示了程序员工作的实际状况:优秀程序员的生产力远超平均水平,可达20到100倍之多;好程序员会花大量时间思考而非仅仅写代码;软件项目容易因持续变更而腐化,损害其完整性。
[size=medium]My experience as a programmer has taught me a few things about writing software. Here are some things that people might find surprising about writing code:

  A programmer spends about 10-20% of his time writing code, and most programmers write about 10-12 lines of code per day that goes into the final product, regardless of their skill level. Good programmers spend much of the other 90% thinking, researching, and experimenting to find the best design. Bad programmers spend much of that 90% debugging code by randomly making changes and seeing if they work.
  “A great lathe operator commands several times the wage of an average lathe operator, but a great writer of software code is worth 10,000 times the price of an average software writer.” –Bill Gates

  A good programmer is ten times more productive than an average programmer. A great programmer is 20-100 times more productive than the average. This is not an exaggeration – studies since the 1960′s have consistently shown this. A bad programmer is not just unproductive – he will not only not get any work done, but create a lot of work and headaches for others to fix.

  Great programmers spend little of their time writing code – at least code that ends up in the final product. Programmers who spend much of their time writing code are too lazy, too ignorant, or too arrogant to find existing solutions to old problems. Great programmers are masters at recognizing and reusing common patterns. Good programmers are not afraid to refactor (rewrite) their code constantly to reach the ideal design. Bad programmers write code which lacks conceptual integrity, non-redundancy, hierarchy, and patterns, and so is very difficult to refactor. It’s easier to throw away bad code and start over than to change it.

  Software obeys the laws of entropy, like everything else. Continuous change leads to software rot, which erodes the conceptual integrity of the original design. Software rot is unavoidable, but programmers who fail to take conceptual integrity into consideration create software that rots so so fast that it becomes worthless before it is even completed. Entropic failure of conceptual integrity is probably the most common reason for software project failure. (The second most common reason is delivering something other than what the customer wanted.) Software rot slows down progress exponentially, so many projects face exploding timelines and budgets before they are killed.

  A 2004 study found that most software projects (51%) will fail in a critical aspect, and 15% will fail totally. This is an improvement since 1994, when 31% failed.

  Although most software is made by teams, it is not a democratic activity. Usually, just one person is responsible for the design, and the rest of the team fills in the details.

  Programming is hard work. It’s an intense mental activity. Good programmers think about their work 24/7. They write their most important code in the shower and in their dreams. Because the most important work is done away from a keyboard, software projects cannot be accelerated by spending more time in the office or adding more people to a project.[/size]
【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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