[ZZ]为什么开发者不能准确估计开发时间

本文探讨了软件开发中无法精确估计任务时间的原因,指出其本质在于创造新知识的过程。通过引用《Principles of Product Development Flow》等著作,文章提供了关于流程管理和时间预测的见解,并分享了一个实际项目的开发时间数据。此外,讨论了估计时间的常见问题,如估算膨胀、不可预知性以及团队完成任务的时间是否提前等问题。

Why Can't Developers Estimate Time?

A few interesting points came up on a mailing list thread I was involved in. Here are a few of them. The original comments are presented as sub-headers / quoted blocks, with my response below. This isn't a thorough look at the issues involved, but what I thought were relevant responses. Note: I've done some editing to improve the flow and to clarify a few things.

Why can't developers estimate time?

We can't estimate the time for any individual task in software development because the nature of the work is creating new knowledge.

The goal of software development is to automate processes. Once a process is automated, it can be run repeatedly, and in most cases, in a predictable time. Source code is like a manufacturing blueprint, the computer is like a manufacturing plant, the inputs (data) are like raw materials, and the outputs (data) are like finished goods. To use another analogy, the reason Starbucks makes drinks so quickly and repeatably is because they invested a lot of time in the design of the process, which was (and is, ongoing) a complex and expensive task. Individual Starbucks franchises don't have to re-discover this process, they just buy the blueprint. I'll leave it as an exercise to the reader to infer my opinion of the Costa coffee-making process.

It's not actually always a problem that development time is unpredictable, because the flipside is that so is the value returned. A successful piece of software can make or save vastly more than its cost. Tom DeMarco argues for focussing on the high value projects for exactly this reason. Note that this does require a value-generation mindset, rather than the currently-prevalent cost-control mindset. This is a non-trivial problem.

By far the best explanation I've read of variability and how to exploit it for value is Don Reinertsen's Principles of Product Development Flow, which is pretty much the adopted "PatchSpace Bible" for day-to-day process management. And when I say "by far the best", I mean by an order of magnitude above pretty much everything else I've read, apart from the Theory of Constraints literature.

Here is the data from my last development project. (Histogram generated in R with 5-hour buckets: the horizontal axis shows the duration in hours for the user stories - 0-5 hours, 5-10 hours, etc; the vertical axis is the number of stories that took that duration). We worked in 90 minute intervals and journaled the work on Wave, so we knew task durations to a pretty fine resolution. (We did this for both client communication and billing purposes.) The result: our development times were about as predictable as radioactive decay, but they were very consistently radioactive. Correlation with estimates was so poor I refused to estimate individual tasks, as it would have been wilfully misleading, but we had enough data to make sensible aggregates.

 

Stories_dev_time_histogram

 

Rule of thumb: take the estimates of a developer, double it and add a bit

The double-and-add-a-bit rule is interesting. When managers do this, how often are tasks completed early? We generally pay much more attention to overruns than underruns. If a team is not completing half of its tasks early, it is padding the estimates, and that means trading development cycle time for project schedule. Cycle time is usually much more valuable than predictability, as it means getting to market sooner. Again, see Reinertsen's work, the numbers can come out an order of magnitude apart.

Also, this is the basis for Critical Chain project management, which halves the "safe" estimates to condense the timescale, and puts the remaining time (padding on individual tasks) at the end, as a "project buffer". This means that Parkinson's Law doesn't cause individual tasks to expand unduly. I'm unconvinced that Critical Chain is an appropriate method for software though, as the actual content of development work can change significantly, as feedback and learning improves the plan.

People in general just make shit up

It's not just developers that are bad with estimates either. Everyone at some point is just winging it because it's something they've never done before and won't be able to successfully make a judgement until they have.

 

As a community we need to get away from this. If we don't know, we don't know, and we need to say it. Clients who see regular progress on tasks they were made aware were risky (and chose to invest in) have much more trust in their team than clients whose teams make shit up. It's true! Srsly. Don't just take my word for it, though - read David Anderson's Kanban.

 

Estimating is a very important skill and should be taught more in junior dev roles

I propose an alternative: what we need to do is teach to junior devs the meaning of done. If estimation problems are bad enough, finding out at some indeterminate point in the future that something went out unfinished (possibly in a rush to meet a commitment … I mean - estimate!) blows not only that estimate out of the water, but the schedule of all the current work in process too. This is very common, and can cause a significant loss of a development team's capacity.

内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值