Barry Boehm’s 1972 Predictions of Higher Software Costs Changed The Course of Computing
In working on a paper honoring Dr. Barry Boehm, I ran across this document which describes some of the trials and tribulations of the initial common high order programming initiative. One thing the article identified is Dr. Boehm’s report that software costs were going to outrun hardware costs and described high costs and unreliability. The report also described one of the major goals of the initiative was reducing software costs.
“The language should facilitate the reduction of software costs. The costs must be reckoned on the total burden of the life cycle including maintenance, not just the cost of production or program writing.”
Barry has done more to legitimize the science of parametric estimation than almost anyone, tracing its roots to 1972 or earlier.
Even SEER-SEM, which has roots seperate from Boehm’s COCOMO, can trace back to the time when it aligned some input parameters with Boehm’s definitions to promote consistency in the industry. And Barry has been very supportive of SEER over the years, including writing the forward of my book which is included below:
Boehm Forward to Galorath / Evans Book
Many people who acquire a software estimation model assume that its use involves furnishing it with a few project parameters, taking the resulting outputs, and plugging them into proposals, project plans, work breakdown structures, budgets, and schedules.
However, people experienced in software estimation have learned that running the model is just the small tip of a very large iceberg of activities essential to successful estimates, projects, and enterprises. Those activities include:
- Identifying what is being estimated and why. One early cost model’s answer to questions asking whether the model estimates included costs of management or quality assurance was, “What would you like the estimates to include?” This is not a strong confidence builder.
- Defining the project’s requirements and design as well as possible. If you don’t know whether a product function will be fulfilled by new, modified, or commercial software, your estimates can be way off.
- Using several perspectives to estimate software size, cost, and schedule. Otherwise, there is no way to tell whether your estimates are reasonably accurate or not.
- Identifying ranges of uncertainty in the project parameters. This enables techniques such as Monte Carlo analysis to determine the likelihood of finishing within a given budget or schedule. Just using a “most likely” point estimate will overrun roughly half of the time.
- Performing a business case relating estimated costs to estimated benefits and return on investment. Otherwise, scarce resources are likely to be spent on low-payoff capabilities.
- Negotiation of tradeoffs among cost, schedule, quality, performance, and functionality. Optimizing on one of these parameters at the expense of the others has been the source of many failed projects.
- Matching desired capabilities to available budgets, schedules, and skilled personnel. Neglecting this activity has been the source of many project overruns.
- Tracking not only cost and progress with respect to original cost and schedule estimates, but also changes in cost driver parameters. Tracking to obsolete estimates has been the source of many painful surprises.
The authors of this book, Daniel Galorath and Michael Evans, are both highly experienced estimators and project managers. Much of this book is devoted to their helping you understand and apply these “under the tip of the iceberg” activities. Their 10-step approach to software estimation provides a logical progression of estimation activities that help you avoid these sources of project overruns and failures.
The book naturally focuses on the use of Galorath Incorporated’s SEER-SEM software cost model to address software estimation activities. But it does so in the context of advice to use multiple perspectives in size, cost, and schedule estimation. And it provides a lot of valuable information about the SEER-SEM cost and size drivers that are often not available for proprietary cost models. It also shows how to use your estimation and project tracking data to improve your estimation accuracy and to identify best investments for improving your software productivity and cycle time. Investing in acquiring this book and following its advice is highly likely to provide you with a robust return on your investment.
Another veteran of the consequences of neglecting the bottom of the iceberg
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