不懂技术的人不要对懂技术的人说这很容易实现
不懂技术的人不要对懂技术的人说这很容易实现
“这个网站相当简单，所有你需要做的就是完成X，Y，Z。你看起来应该是技术很好，所以，我相信，你不需要花费太多时间就能把它搭建起来。”
我时不时的就会收到这样的Email。写这些邮件的人几乎都是跟技术不沾边的人，或正在研究他们的第一个产品。起初，当听到人们这样的话，我总是十分的恼怒。他们在跟谁辩论软件开发所需要的时间?但后来我意识到，即使我自己对自己的项目预测要花去多少开发时间,我也是一筹莫展。如果连我自己都做不好，我何必对那些人恼怒呢?
真正让我郁闷的不是他们预估的错误。问题在于他们竟然认为自己可以做出正确的估计。作为开发人员，我们经常会发现，在软件开发的问题上，一个外行人会很自然的把复杂的事情估计的很简单。
这并不是为我们的愤怒找借口。但这引起了另外一个有趣的问题：为什么我们天生的预测复杂性的能力在遇到编程问题时会失灵?
为了回答这个问题，让我们来认识一下我们的大脑如何估计事情的。有些事情对于一些没有经验的人也很容易预估正确，但有些事情则不然。
我们来想想观看一个人弹吉他。即使你从来没有弹过吉他，在观看了一场弹奏《玛丽有只小羊羔(Mary had a Little Lamb)》的吉他表演后，你也能大概推测出这很简单，一个人不需要太高的技术就能演奏出来。同样，当观看了有人演奏D大调的《卡农(Pachabel’s Canon)》后，你也很容易推测出，这很复杂，需要很长时间的练习才能演奏的出来。
为什么我们能够很迅速准确的预估这两首曲子的复杂性呢?这是跟我们用来判断一个事情简单和还是复杂的方法有关的。我们的大脑有一些现成的模式来完成这些事情，首先一个就是根据速度。这种情况下，大脑会辨别每秒钟演奏的东西。根据每秒钟演奏了多少东西，我们很容易有一个直观的判断曲子的复杂度。因为用吉他演奏一首歌是一种物理过程，一种感官上的活动，我们的大脑很容易依此来推测速度，继而转换成复杂度。
我们还有另外一个天生的推测依据：体积。想想把一个帐篷和一栋公寓放在一起对比。即使一个人从来没有学过建筑学，他也能告诉你通常设计和建造一个帐篷会比设计和建造一栋公寓要简单。为什么?因为我们天生的会使用物理体积作为事物复杂性的一个指标。
当然。上面说的这两种逻辑分析并不是总是100%的有效。但大多数情况下，人们就是这样干，而且很成功。大多数情况中，我们在对物理过程评估时，我们的大脑会对物理事物进行有效的关联，不需要依赖之前的经验。
现在让我们来谈谈软件。当一个不懂技术的人试图对软件开发时间进行评估时，有两个很基本的直观指标在辅助他们：以体积为指标的复杂度和以速度为指标的复杂度。但他们没有意识到，软件跟他们想象的不一样。软件本质上不是有形物质。没有体积和速度。它的极小的组成部分可能会时不时的在电脑屏幕上闪现。正因为如此，当面对开发一个web应用时(或任何类型的软件)，我们的基本直观感觉失效了。
这第一点，速度，很显然根本不可能被外行人拿来对软件进行评估。于是很自然的，他们倾向于使用体积指标进行评估。要么是根据描述文档的页数，要么是根据软件的功能用例数或特征数。
有时候，这种评估手段确实有效!当面对一个静态网站，没有特别的设计要求，外行人很容易用这种方法估计出开发时间。但是，通常情况下，对于软件开发，体积并不能真实有效的反映复杂度。
不幸的是，对于软件的复杂度，唯一有效的推测方法是依据经验。而且还不是时时都好用。作为一个程序员，我知道，根据我之前开发过的相似的功能特征，我可以估计出现在的这些功能特征各自要多少开发时间。然后，我把总时间加起来，这就得到了完成整个项目需要的大致时间。然而，事实情况中，每个项目在开发过程中都遇到二、三个瓶颈。这些瓶颈会肆意的消耗程序员的大量时间，你在遇到它们之前根本不会有所预见。它们会拖住整个项目，致使工期延后数周甚至数月。
这些是没有经验的人在评估复杂度时不会理解的。他们不明白在其他事情上都很灵的方法，为什么放到软件开发上就不灵了。所以，下一次当你听到有人说“我想你几天时间就能把它开发出来”时，不管是谁说的，都不要懊恼。深呼吸一下，告诉他这篇文章的地址，自己该干什么还干什么。
您可能感兴趣的文章
- 在php中分别使用curl的post提交数据的方法和get获取网页数据的方法总结
- js屏蔽鼠标键盘事件(包括鼠标右键,方向键,退格键,F5刷新键等),兼容IE和firefox
- js限制只能输入英文字母和数字,不能输入中文和其他特殊字符的办法
- phpmyadmin 配置文件详细的解释说明
- 总结mysql服务器查询慢原因与解决方法
- PHP ob_start()函数的功能要点详细说明
- 网站上多种url都能访问同样的内容是件危险的事
- 右击桌面新建文本文档不见了，如何恢复
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
The Stable Diffusion 3 paper is finally released, and the architectural details are revealed. Will it help to reproduce Sora?
Mar 06, 2024 pm 05:34 PM
StableDiffusion3’s paper is finally here! This model was released two weeks ago and uses the same DiT (DiffusionTransformer) architecture as Sora. It caused quite a stir once it was released. Compared with the previous version, the quality of the images generated by StableDiffusion3 has been significantly improved. It now supports multi-theme prompts, and the text writing effect has also been improved, and garbled characters no longer appear. StabilityAI pointed out that StableDiffusion3 is a series of models with parameter sizes ranging from 800M to 8B. This parameter range means that the model can be run directly on many portable devices, significantly reducing the use of AI
Have you really mastered coordinate system conversion? Multi-sensor issues that are inseparable from autonomous driving
Oct 12, 2023 am 11:21 AM
The first pilot and key article mainly introduces several commonly used coordinate systems in autonomous driving technology, and how to complete the correlation and conversion between them, and finally build a unified environment model. The focus here is to understand the conversion from vehicle to camera rigid body (external parameters), camera to image conversion (internal parameters), and image to pixel unit conversion. The conversion from 3D to 2D will have corresponding distortion, translation, etc. Key points: The vehicle coordinate system and the camera body coordinate system need to be rewritten: the plane coordinate system and the pixel coordinate system. Difficulty: image distortion must be considered. Both de-distortion and distortion addition are compensated on the image plane. 2. Introduction There are four vision systems in total. Coordinate system: pixel plane coordinate system (u, v), image coordinate system (x, y), camera coordinate system () and world coordinate system (). There is a relationship between each coordinate system,
This article is enough for you to read about autonomous driving and trajectory prediction!
Feb 28, 2024 pm 07:20 PM
Trajectory prediction plays an important role in autonomous driving. Autonomous driving trajectory prediction refers to predicting the future driving trajectory of the vehicle by analyzing various data during the vehicle's driving process. As the core module of autonomous driving, the quality of trajectory prediction is crucial to downstream planning control. The trajectory prediction task has a rich technology stack and requires familiarity with autonomous driving dynamic/static perception, high-precision maps, lane lines, neural network architecture (CNN&GNN&Transformer) skills, etc. It is very difficult to get started! Many fans hope to get started with trajectory prediction as soon as possible and avoid pitfalls. Today I will take stock of some common problems and introductory learning methods for trajectory prediction! Introductory related knowledge 1. Are the preview papers in order? A: Look at the survey first, p
DualBEV: significantly surpassing BEVFormer and BEVDet4D, open the book!
Mar 21, 2024 pm 05:21 PM
This paper explores the problem of accurately detecting objects from different viewing angles (such as perspective and bird's-eye view) in autonomous driving, especially how to effectively transform features from perspective (PV) to bird's-eye view (BEV) space. Transformation is implemented via the Visual Transformation (VT) module. Existing methods are broadly divided into two strategies: 2D to 3D and 3D to 2D conversion. 2D-to-3D methods improve dense 2D features by predicting depth probabilities, but the inherent uncertainty of depth predictions, especially in distant regions, may introduce inaccuracies. While 3D to 2D methods usually use 3D queries to sample 2D features and learn the attention weights of the correspondence between 3D and 2D features through a Transformer, which increases the computational and deployment time.
The first multi-view autonomous driving scene video generation world model | DrivingDiffusion: New ideas for BEV data and simulation
Oct 23, 2023 am 11:13 AM
Some of the author’s personal thoughts In the field of autonomous driving, with the development of BEV-based sub-tasks/end-to-end solutions, high-quality multi-view training data and corresponding simulation scene construction have become increasingly important. In response to the pain points of current tasks, "high quality" can be decoupled into three aspects: long-tail scenarios in different dimensions: such as close-range vehicles in obstacle data and precise heading angles during car cutting, as well as lane line data. Scenes such as curves with different curvatures or ramps/mergings/mergings that are difficult to capture. These often rely on large amounts of data collection and complex data mining strategies, which are costly. 3D true value - highly consistent image: Current BEV data acquisition is often affected by errors in sensor installation/calibration, high-precision maps and the reconstruction algorithm itself. this led me to
GSLAM | A general SLAM architecture and benchmark
Oct 20, 2023 am 11:37 AM
Suddenly discovered a 19-year-old paper GSLAM: A General SLAM Framework and Benchmark open source code: https://github.com/zdzhaoyong/GSLAM Go directly to the full text and feel the quality of this work ~ 1 Abstract SLAM technology has achieved many successes recently and attracted many attracted the attention of high-tech companies. However, how to effectively perform benchmarks on speed, robustness, and portability with interfaces to existing or emerging algorithms remains a problem. In this paper, a new SLAM platform called GSLAM is proposed, which not only provides evaluation capabilities but also provides researchers with a useful way to quickly develop their own SLAM systems.
'Minecraft' turns into an AI town, and NPC residents role-play like real people
Jan 02, 2024 pm 06:25 PM
Please note that this square man is frowning, thinking about the identities of the "uninvited guests" in front of him. It turned out that she was in a dangerous situation, and once she realized this, she quickly began a mental search to find a strategy to solve the problem. Ultimately, she decided to flee the scene and then seek help as quickly as possible and take immediate action. At the same time, the person on the opposite side was thinking the same thing as her... There was such a scene in "Minecraft" where all the characters were controlled by artificial intelligence. Each of them has a unique identity setting. For example, the girl mentioned before is a 17-year-old but smart and brave courier. They have the ability to remember and think, and live like humans in this small town set in Minecraft. What drives them is a brand new,
Review! Deep model fusion (LLM/basic model/federated learning/fine-tuning, etc.)
Apr 18, 2024 pm 09:43 PM
In September 23, the paper "DeepModelFusion:ASurvey" was published by the National University of Defense Technology, JD.com and Beijing Institute of Technology. Deep model fusion/merging is an emerging technology that combines the parameters or predictions of multiple deep learning models into a single model. It combines the capabilities of different models to compensate for the biases and errors of individual models for better performance. Deep model fusion on large-scale deep learning models (such as LLM and basic models) faces some challenges, including high computational cost, high-dimensional parameter space, interference between different heterogeneous models, etc. This article divides existing deep model fusion methods into four categories: (1) "Pattern connection", which connects solutions in the weight space through a loss-reducing path to obtain a better initial model fusion
