The academic research community—along with a small number of industry research groups—has traditionally addressed many of the core technical problems related to software producibility. The academic value proposition has several direct components: The first is workforce. University graduates are the core of the engineering workforce. The most talented and highly trained graduates—those who contribute to innovation in a primary way—tend to come from PhD programs. More generally, the research community generates a steady supply of people—graduates at all levels—educated at the frontiers of current knowledge in important areas of specialization. The economics of these programs depend on externally funded research projects. That is, unlike bachelor’s and master’s enrollments, the production of PhD graduates by universities is in direct proportion to sponsored research. It is perhaps too obvious to point this out, but cleared individuals with top technical qualifications are most likely to be graduates of U.S. universities.

The second component is new knowledge. The style of computer science and software research, historically, has focused on the creation of scientific understanding that is both fundamental and applicable. This is in keeping with the “boundlessness” of software as described in Chapter 1.3 Although industry plays a limited role in performing research relevant to fundamental open problems, there is no institution in the United States other than the research community, located primarily at universities, that focuses on broad and often non-appropriable advancements to knowledge that are directly relevant to practice. Indeed, major corporate labs that have historically supported non-appropriable and open-publication research as a significant part of their overall portfolios (such as Bell Labs and Xerox PARC) have been restructured or scaled back in recent years. This scaling back of private-sector research is due to numerous factors, including a loss by many players of safe monopoly status, analogous to that which enabled Bell Labs to thrive. This creates greater internal pressure on laboratory managers to create measurable return on investment (ROI) cases for research projects. This is particularly challenging for software producibility research, which is often focused on creating new measures of “return” rather than on incremental advances according to readily measurable criteria. This increases the significance of the role of academic research, government laboratories, and federally funded research and developments centers (FFRDCs). This is not to say that major research effort in software producibility is not underway in industry. At Microsoft and IBM, particularly, there is aggressive and forward-looking work in this area that is having significant influence across the industry.

Academic research and development (R&D) is also a major generator of entrepreneurial activity in information technology (IT).4 The small companies in that sector have an important role in developing and market testing new ideas. The infrastructure to support these ventures is an important differentiator of the U.S. innovation system. This infrastructure includes university intellectual property and people supported by university R&D projects. These companies may sometimes disrupt the comfortable market structures of incumbent firms, but arguably not in the same way as do competition or foreign innovation. Regardless, weak incumbents tend to fall by the wayside when there is any disruption. Strong incumbents become stronger. This constant disruption is a characteristic of the more than half-century of IT innovation. It is essential that the DoD itself be effective as a strong incumbent that is capable of gaining strength through disruptive innovations, rather than being a victim (see below). The intelligence community’s Disruptive Technology Office (DTO, now part of Intelligence Advanced Projects Research Agency5) can be presumed to have been founded upon this model.

A third area of value provided by university-based R&D (and industrial lab R&D as well) is surprise reduction. Computing technology is continuing to experience very rapid change, at a rate that has been


This is the fundamental yet eventually useful knowledge in what Donald Stokes has called Pasteur’s Quadrant. See Donald E. Stokes, 1997, Pasteur’s Quadrant—Basic Science and Technological Innovation, Washington, DC: Brookings Institution Press.


The committee uses “information technology” or “IT” to refer to the full range of computing and information technology areas in the scope of the NITRD multi-agency coordination activity (see Last accessed August 20, 2010).


See Last accessed August 20, 2010.

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