Arguments Against Underestimation

Underestimation creates numerous problems—some obvious, some not so obvious.

Reduced effectiveness of project plans Low estimates undermine effective planning by feeding bad assumptions into plans for specific activities. They can cause planning errors in the team size , such as planning to use a team that's smaller than it should be. They can undermine the ability to coordinate among groups—if the groups aren't ready when they said they would be, other groups won't be able to integrate with their work.

If the estimation errors caused the plans to be off by only 5% or 10%, those errors wouldn't cause any significant problems. But numerous studies have found that software estimates are often inaccurate by 100% or more (Lawlis, Flowe, and Thordahl 1995; Jones 1998; Standish Group 2004; ISBSG 2005). When the planning assumptions are wrong by this magnitude, the average project's plans are based on assumptions that are so far off that the plans are virtually useless.

Statistically reduced chance of on-time completion Developers typically estimate 20% to 30% lower than their actual effort (van Genuchten 1991). Merely using their normal estimates makes the project plans optimistic. Reducing their estimates even further simply reduces the chances of on-time completion even more.

Poor technical foundation leads to worse -than-nominal results A low estimate can cause you to spend too little time on upstream activities such as requirements and design. If you don't put enough focus on requirements and design, you'll get to redo your requirements and redo your design later in the project—at greater cost than if you'd done those activities well in the first place (Boehm and Turner 2004, McConnell 2004a). This ultimately makes your project take longer than it would have taken with an accurate estimate.

Destructive late-project dynamics make the project worse than nominal Once a project gets into "late" status, project teams engage in numerous activities that they don't need to engage in during an "on-time" project. Here are some examples:

• More status meetings with upper management to discuss how to get the project back on track.

• Frequent reestimation, late in the project, to determine just when the project will be completed.

• Apologizing to key customers for missing delivery dates (including attending meetings with those customers).

• Preparing interim releases to support customer demos, trade shows, and so on. If the software were ready on time, the software itself could be used, and no interim release would be necessary.

• More discussions about which requirements absolutely must be added because the project has been underway so long.

• Fixing problems arising from quick and dirty workarounds that were implemented earlier in response to the schedule pressure.

The important characteristic of each of these activities is that they don't need to occur at all when a project is meeting its goals. These extra activities drain time away from productive work on the project and make it take longer than it would if it were estimated and planned accurately.

Weighing the Arguments

Goldratt's Student Syndrome can be a factor on software projects, but I've found that the most effective way to address Student Syndrome is through active task tracking and buffer management (that is, project control), similar to what Goldratt suggests, not through biasing the estimates.

As Figure 3-1 shows, the best project results come from the most accurate estimates (Symons 1991). If the estimate is too low, planning inefficiencies will drive up the actual cost and schedule of the project. If the estimate is too high, Parkinson's Law kicks in.

Figure 3-1: The penalties for underestimation are more severe than the penalties for overestimation, so, if you can't estimate with complete accuracy, try to err on the side of overestimation rather than underestimation.