software development and also that software engineers will find the statistical emphasis refreshing and stimulating. This report also addresses the important issues of training and education of software engineers in the statistical sciences and of statisticians with an interest in software engineering.

At the panel's information-gathering forum in October 1993, 12 invited speakers described their views on topics that are considered in detail in Chapters 2 through 6 of this report. One of the speakers, John Knight, pointed out that the date of the forum coincided nearly to the day with the 25th anniversary of the Garmisch Conference (Randell and Naur, 1968), a NATO-sponsored workshop at which the term "software engineering" is generally accepted to have originated. The particular irony of this coincidence is that it is also generally accepted that although much more ambitious software systems are now being built, little has changed in the relative ability to produce software with predictable quality, costs, and dependability. One of the original Garmisch participants, A.G. Fraser, now associate vice president in the Information Sciences Research Division at AT&T Bell Laboratories, defends the apparent lack of progress by the reminder that prior to Garmisch, there was no "collective realization" that the problems individual organizations were facing were shared across the industry—thus Garmisch was a critical first step toward addressing issues in software production. It is hoped that this report will play a similar role in seeding the field of statistical software engineering by indicating opportunities for statistical thinking to help increase understanding, as well as the productivity and quality, of software and software production.

In preparing this report, the panel struggled with the problem of providing the "big picture" of the software production process, while simultaneously attempting to highlight opportunities for related research on statistical methods. The problems facing the software engineering field are indeed broad, and nonstatistical approaches (e.g., formal methods for verifying program specifications) are at least as relevant as statistical ones. Thus this report tends to emphasize the larger context in which statistical methods must be developed, based on the understanding that recognition of the scope and the boundaries of problems is essential to characterizing the problems and contributing to their solution. It must be noted at the outset, for example, that software engineering is concerned with more than the end product, namely, code. The production process that results in code is a central concern and thus is described in detail in the report. To a large extent, the presentation of material mirrors the steps in the software development process. Although currently the single largest area of overlap between statistics and software engineering concerns software testing (which implies that the code exists), it is the panel's view that the largest contributions to the software engineering field will be those affecting the quality and productivity of the processes that precede code generation.

The panel also emphasizes that the process and methods described in this report pertain to the case of new software projects, as well as to the more ordinary circumstance of evolving software projects or "legacy systems." For instance, the software that controls the space shuttle flight systems or that runs modern telecommunication networks has been evolving for several decades. These two cases are referred to frequently to illustrate software development concepts and current practice, and although the software systems may be uncharacteristically large, they are arguably forerunners of what lies ahead in many applications. For example, laser printer software is witnessing an order-of-magnitude (base-10) increase in size with each new release.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement