a level of exposure that enables researchers to engage productively without compromising core intellectual property. The DoD has a track record of success in this regard as well.

For software producibility research, a different kind of access is needed. Certainly, the success of large-scale production-quality open source has afforded researchers great opportunity not only to experiment with large code bases, but also to undertake longitudinal and organizational analyses of larger projects. This has been enabled by the sophistication of the tools—code management systems, defect databases, designs and models, test cases. These projects are comparable in scale and functionality to commercial software and have greatly assisted the software engineering community in its research. Additionally, commercial firms are affording researchers greater access to proprietary code bases for experimentation and analysis. An early and significant example is work by Gail Murphy in which she assessed consistency of an as-built code base with architectural intent.8 She studied both an open source project and a proprietary project. If security and commercial ownership issues could be resolved (perhaps by clearing selected researchers), members of the research community would benefit greatly from access to DoD-related artifacts, including surrogates and “sanitized” artifacts that omit critical algorithms and/or data. Regardless of access, the committee recommends improved data collection to support analysis (see Recommendation 2-2).


The Impact of Past Investments

Software development has changed and, for the most part, improved considerably during the past several decades. Software systems have grown in size and complexity and are now an integrated component of every aspect of our society, including finance, transportation, communication, and health care. Since the 1960s, Moore’s Law has correctly predicted the tremendous growth in the number of transistors on chips and, generally speaking, the extent of hardware-delivered computing power. An analogous growth has occurred in the size and power of software systems if machine-level instructions, rather than transistors, are the measure of growth.9,10,11 Today’s systems are built using high-level languages and numerous software library components, developed using sophisticated tools and frameworks, and executed with powerful runtime support capabilities.

Research in software engineering, programming technologies, and other areas of computer science has been a catalyst for many of these advances. Nearly all of this research was undertaken at research universities as part of federal programs led by DARPA, the National Science Foundation (NSF), and the Service basic (category 6.1) research programs of the Office of Naval Research, Air Force Office of Science Research, and Army Research Office.

Three illustrations of the impact of federal sponsorship (in academia and industry) that is specifically related to software engineering are presented in Box 5.2. These illustrations, drawn from a study undertaken by Osterweil et al.,12 complement the analyses of the NRC reports cited above relating to research impacts on practice and on the IT economy.


Gail Murphy, 1995, “Software Reflexion Models: Bridging the Gap Between Source and High-level Models,” Proceedings of the Third ACM SIGSOFT Symposium on Foundations of Software Engineering, Washington, DC, October 10-13, pp. 18-28.


Barry Boehm, 1999, “Managing Software Productivity and Reuse,” IEEE Computer September, 32(9):111-113.


Mary Shaw, 2002, “The Tyranny of Transistors: What Counts about Software?” Proceedings of the Fourth Workshop on Economics-Driven Software Engineering Research, IEEE Computer Society, Orlando, FL, May 19-25, pp. 49-51.


Barry Boehm, 2006, “A View of 20th and 21st Century Software engineering,” Proceedings of the 28th International Conference on Software Engineering, ACM, Shanghai, China, May 20-28, pp. 12-29.


Leon J. Osterweil, Carlo Ghezzi, Jeff Kramer, Alexander L. Wolf, 2008, “Determining the Impact of Software Engineering Research on Practice,” IEEE Computer 41(3):39-49.

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