By Steve McConnell
The software program is infamous for its negative estimates. For years, Code Complete's Steve McConnell has been attempting to repair that. In software program Estimation, he is introduced jointly all he is realized via instructing hundreds of thousands of software program execs and development one of many world's major software program estimation instruments. this is often the definitive therapy: either formal suggestions and an important "rules of thumb.McConnell walks via assets of errors, finds universal estimating pitfalls, and exhibits how one can increase accuracy by utilizing a number of easy methods to refine your projections. you will the right way to "decompose" initiatives into smaller components (for example, modules rather than systems); after which recompose these parts into one coherent estimate.Next, he systematically addresses the demanding situations of estimating undertaking dimension, attempt, and agenda. final yet no longer least, you will discover lifelike suggestions on providing estimates to administration, and surviving these inevitable, dreaded negotiations.
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Extra info for Software Estimation: Demystifying the Black Art
Chapters 21 through 23 discuss basic estimation concepts, general estimation techniques, and allowing for inaccuracy in estimates. Tockey includes a detailed discussion of how to build your own Cone of Uncertainty. Wiegers, Karl. More About Software Requirements: Thorny Issues and Practical Advice. Redmond, WA: Microsoft Press, 2006. Wiegers, Karl. Software Requirements, Second Edition. Redmond, WA: Microsoft Press, 2003. In these two books, Wiegers describes numerous practices that help elicit good requirements in the first place, which substantially reduces requirements volatility later in a project.
What will the quality level of the Telephone Number Checker be? ) • How long will it take to debug and correct mistakes made in the implementation of the Telephone Number Checker? (Individual performance among different programmers with the same level of Chapter 4: Where Does Estimation Error Come From? 31 32 Chapter 4: Where Does Estimation Error Come From? ) As you can see just from this short list of uncertainties, potential differences in how a single feature is specified, designed, and implemented can introduce cumulative differences of a hundredfold or more in implementation time for any given feature.
The reason the estimate contains variability is that the software project itself contains variability. The only way to reduce the variability in the estimate is to reduce the variability in the project. One misleading implication of this common depiction of the Cone of Uncertainty is that it looks like the Cone takes forever to narrow—as if you can't have very good estimation accuracy until you're nearly done with the project. Fortunately, that impression is created because the milestones on the horizontal axis are equally spaced, and we naturally assume that the horizontal axis is calendar time.