must play a leadership role in justifying that resources are needed to acquire and maintain high-quality and relevant data.

The panel conjectures that the use of adequate metrics and data of good quality is the primary differentiator between successful, productive software development organizations and those that are struggling. Although the single largest area of overlap between statistics and software engineering currently concerns software development and production, it is the panel's view that the largest contributions of statistics to software engineering will be those affecting the quality and productivity of front-en d processes, that is, processes that precede code generation. One of the biggest impacts that the statistical community can make in software engineering is to combine information across software engineering projects as a means of evaluating effects of technology, language, organization, and process.


Following an introductory opening chapter intended to familiarize readers with basic statistical software engineering concepts and concerns, a case study of the National Aeronautics and Space Administration (NASA) space shuttle flight control software is presented in Chapter 2 to illustrate some of the statistical issues in software engineering. Chapter 3 describes a well-known general software production model and associated statistical issues and approaches. A critique of some current applications of statistics and software engineering is presented in Chapter 4. Chapter 5 discusses a number of statistical challenges arising in software engineering, and the panel's closing summary and conclusions appear in Chapter 6.


In comparison with other engineering disciplines, software engineering is still in the definition stage. Characteristics of established disciplines include having defined, tested, credible methodologies for practice, assessment, and predictability. Software engineering combines application domain knowledge, computer science, statistics, behavioral science, and human factors issues. Statistical challenges in software engineering discussed in this report include the following:

  • Generalizing particular statistical software engineering experimental results to other settings and projects,

  • Scaling up results obtained in academic studies to industrial settings,

  • Combining information across software engineering projects and studies,

  • Adopting exploratory data analysis and visualization techniques,

  • Educating the software engineering community regarding statistical approaches and data issues,

  • Developing methods of analysis to cope with qualitative variables,

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