The Panel on Statistical Methods for Testing and Evaluating Defense Systems had a broad mandate-to examine the use of statistics in conjunction with defense testing. This involved examining methods for software testing, reliability test planning and estimation, validation of modeling and simulation, and use of modem techniques for experimental design. Given the breadth of these areas, including the great variety of applications and special issues that arise, making a contribution in each of these areas required that the Panel's work and recommendations be at a relatively general level. However, a variety of more specific research issues were either brought to the Panel's attention by members of the test and acquisition community, e.g., what was referred to as Dubin's challenge (addressed in the Panel's interim report), or were identified by members of the panel. In many of these cases the panel thought that a more in-depth analysis or a more detailed application of suggestions or recommendations made by the Panel would either be useful as input to its deliberations or could be used to help communicate more individual views of members of the Panel to the defense test community. This resulted in several research efforts. Given various criteria, especially immediate relevance to the test and acquisition community, the Panel has decided to make available three technical or background papers, each authored by a Panel member jointly with a colleague. These papers are individual contributions and are not a consensus product of the Panel; however, the Panel has drawn from these papers in preparation of its final report: Statistics, Testing, and Defense Acquisition. The Panel has found each of these papers to be extremely useful and they are strongly recommended to readers of the Panel's final report.

The Panel on Statistical Methods for Testing and Evaluating Defense Systems had a broad mandate-to examine the use of statistics in conjunction with defense testing. This involved examining methods for software testing, reliability test planning and estimation, validation of modeling and simulation, and use of modem techniques for experimental design. Given the breadth of these areas, including the great variety of applications and special issues that arise, making a contribution in each of these areas required that the Panel's work and recommendations be at a relatively general level. However, a variety of more specific research issues were either brought to the Panel's attention by members of the test and acquisition community, e.g., what was referred to as Dubin's challenge (addressed in the Panel's interim report), or were identified by members of the panel. In many of these cases the panel thought that a more in-depth analysis or a more detailed application of suggestions or recommendations made by the Panel would either be useful as input to its deliberations or could be used to help communicate more individual views of members of the Panel to the defense test community. This resulted in several research efforts. Given various criteria, especially immediate relevance to the test and acquisition community, the Panel has decided to make available three technical or background papers, each authored by a Panel member jointly with a colleague. These papers are individual contributions and are not a consensus product of the Panel; however, the Panel has drawn from these papers in preparation of its final report: Statistics, Testing, and Defense Acquisition. The Panel has found each of these papers to be extremely useful and they are strongly recommended to readers of the Panel's final report.

A high percentage of defense systems fail to meet their reliability requirements. This is a serious problem for the U.S. Department of Defense (DOD), as well as the nation. Those systems are not only less likely to successfully carry out their intended missions, but they also could endanger the lives of the operators. Furthermore, reliability failures discovered after deployment can result in costly and strategic delays and the need for expensive redesign, which often limits the tactical situations in which the system can be used. Finally, systems that fail to meet their reliability requirements are much more likely to need additional scheduled and unscheduled maintenance and to need more spare parts and possibly replacement systems, all of which can substantially increase the life-cycle costs of a system. Beginning in 2008, DOD undertook a concerted effort to raise the priority of reliability through greater use of design for reliability techniques, reliability growth testing, and formal reliability growth modeling, by both the contractors and DOD units. To this end, handbooks, guidances, and formal memoranda were revised or newly issued to reduce the frequency of reliability deficiencies for defense systems in operational testing and the effects of those deficiencies. "Reliability Growth" evaluates these recent changes and, more generally, assesses how current DOD principles and practices could be modified to increase the likelihood that defense systems will satisfy their reliability requirements. This report examines changes to the reliability requirements for proposed systems; defines modern design and testing for reliability; discusses the contractor's role in reliability testing; and summarizes the current state of formal reliability growth modeling. The recommendations of "Reliability Growth" will improve the reliability of defense systems and protect the health of the valuable personnel who operate them.

The Department of Defense (DoD) recently adopted evolutionary acquisition, a dynamic strategy for the development and acquisition of its defense systems. Evolutionary defense systems are planned, in advance, to be developed through several stages in a single procurement program. Each stage is planned to produce a viable system which could be fielded. The system requirements for each stage of development may be specified in advance of a given stage or may be decided at the outset of that stage's development. Due to the different stages that comprise an evolutionary system, there exists a need for careful reexamination of current testing and evaluation policies and processes, which were designed for single-stage developments. The Office of the Under Secretary of Defense for Acquisition, Technology and Logistics (USD-AT&L) and the Director of Operational Testing and Evaluation (DOT&E) asked the Committee on National Statistics (CNSTAT) of the National Academies to examine the key issues and implications for defense testing from the introduction of evolutionary acquisition. The CNSTAT was charged with planning and conducting a workshop to study test strategies for the evolutionary acquisition. The committee reviewed defense materials defining evolutionary acquisition and interviewed test officials from the three major test service agencies to understand the current approaches used in testing systems procured through evolutionary acquisition. The committee also examined possible alternatives to identify problems in implementation. At the workshop that took place on December 13-14, 2004, the committee tried to answer many questions including: What are the appropriate roles and objectives for testing in an evolutionary environment?, Can a systematic, disciplined process be developed for testing and evaluation in such a fluid and flexible environment?, and Is there adequate technical expertise within the acquisition community to fully exploit data gathered from previous stages to effectively combine information from various sources for test design and analysis?. Testing of Defense Systems in an Evolutionary Acquisition Environment provides the conclusions and recommendations of the CNSTAT following the workshop and its other investigations.

During the past decade and a half, the National Research Council, through its Committee on National Statistics, has carried out a number of studies on the application of statistical methods to improve the testing and development of defense systems. These studies were intended to provide advice to the Department of Defense (DOD), which sponsored these studies. The previous studies have been concerned with the role of statistical methods in testing and evaluation, reliability practices, software methods, combining information, and evolutionary acquisition. Industrial Methods for the Effective Testing and Development of Defense Systems is the latest in a series of studies, and unlike earlier studies, this report identifies current engineering practices that have proved successful in industrial applications for system development and testing. This report explores how developmental and operational testing, modeling and simulation, and related techniques can improve the development and performance of defense systems, particularly techniques that have been shown to be effective in industrial applications and are likely to be useful in defense system development. In addition to the broad issues, the report identifies three specific topics for its focus: finding failure modes earlier, technology maturity, and use of all relevant information for operational assessments.

The Department of Defense (DoD) recently adopted evolutionary acquisition, a dynamic strategy for the development and acquisition of its defense systems. Evolutionary defense systems are planned, in advance, to be developed through several stages in a single procurement program. Each stage is planned to produce a viable system which could be fielded. The system requirements for each stage of development may be specified in advance of a given stage or may be decided at the outset of that stage's development. Due to the different stages that comprise an evolutionary system, there exists a need for careful reexamination of current testing and evaluation policies and processes, which were designed for single-stage developments. The Office of the Under Secretary of Defense for Acquisition, Technology and Logistics (USD-AT&L) and the Director of Operational Testing and Evaluation (DOT&E) asked the Committee on National Statistics (CNSTAT) of the National Academies to examine the key issues and implications for defense testing from the introduction of evolutionary acquisition. The CNSTAT was charged with planning and conducting a workshop to study test strategies for the evolutionary acquisition. The committee reviewed defense materials defining evolutionary acquisition and interviewed test officials from the three major test service agencies to understand the current approaches used in testing systems procured through evolutionary acquisition. The committee also examined possible alternatives to identify problems in implementation. At the workshop that took place on December 13-14, 2004, the committee tried to answer many questions including: What are the appropriate roles and objectives for testing in an evolutionary environment?, Can a systematic, disciplined process be developed for testing and evaluation in such a fluid and flexible environment?, and Is there adequate technical expertise within the acquisition community to fully exploit data gathered from previous stages to effectively combine information from various sources for test design and analysis?. Testing of Defense Systems in an Evolutionary Acquisition Environment provides the conclusions and recommendations of the CNSTAT following the workshop and its other investigations.

Recent rough estimates are that the U.S. Department of Defense (DoD) spends at least $38 billion a year on the research, development, testing, and evaluation of new defense systems; approximately 40 percent of that cost-at least $16 billion-is spent on software development and testing. There is widespread understanding within DoD that the effectiveness of software-intensive defense systems is often hampered by low-quality software as well as increased costs and late delivery of software components. Given the costs involved, even relatively incremental improvements to the software development process for defense systems could represent a large savings in funds. And given the importance of producing defense software that will carry out its intended function, relatively small improvements to the quality of defense software systems would be extremely important to identify. DoD software engineers and test and evaluation officials may not be fully aware of a range of available techniques, because of both the recent development of these techniques and their origination from an orientation somewhat removed from software engineering, i.e., from a statistical perspective. The panel's charge therefore was to convene a workshop to identify statistical software engineering techniques that could have applicability to DoD systems in development.

A comprehensive treatment of systems and software testing using state of the art methods and tools This book provides valuable insights into state of the art software testing methods and explains, with examples, the statistical and analytic methods used in this field. Numerous examples are used to provide understanding in applying these methods to real-world problems. Leading authorities in applied statistics, computer science, and software engineering present state-of-the-art methods addressing challenges faced by practitioners and researchers involved in system and software testing. Methods include: machine learning, Bayesian methods, graphical models, experimental design, generalized regression, and reliability modeling. Analytic Methods in Systems and Software Testing presents its comprehensive collection of methods in four parts: Part I: Testing Concepts and Methods; Part II: Statistical Models; Part III: Testing Infrastructures; and Part IV: Testing Applications. It seeks to maintain a focus on analytic methods, while at the same time offering a contextual landscape of modern engineering, in order to introduce related statistical and probabilistic models used in this domain. This makes the book an incredibly useful tool, offering interesting insights on challenges in the field for researchers and practitioners alike. Compiles cutting-edge methods and examples of analytical approaches to systems and software testing from leading authorities in applied statistics, computer science, and software engineering Combines methods and examples focused on the analytic aspects of systems and software testing Covers logistic regression, machine learning, Bayesian methods, graphical models, experimental design, generalized regression, and reliability models Written by leading researchers and practitioners in the field, from diverse backgrounds including research, business, government, and consulting Stimulates research at the theoretical and practical level Analytic Methods in Systems and Software Testing is an excellent advanced reference directed toward industrial and academic readers whose work in systems and software development approaches or surpasses existing frontiers of testing and validation procedures. It will also be valuable to post-graduate students in computer science and mathematics.

The study of operations research arose during World War II to enhance the effectiveness of weapons and equipment used on the battlefield. Since then, operations research techniques have also been used to solve several sophisticated and complex defense-related problems. Operations Research for Military Organizations is a critical scholarly resource that examines the issues that have an impact on aspects of contemporary quantitative applications of operations research methods in the military. It also addresses innovative applications, techniques, and methodologies to assist in solving defense and military-related problems. Featuring coverage on a broad range of topics such as combat planning, tactical decision aids, and weapon system simulations, this book is geared towards defense contractors, military consultants, military personnel, policy makers, and government departments seeking current research on defense methodologies.

"In the conference report accompanying the National Defense Authorization Act for Fiscal Year 2017, Congress expressed concern that the U.S. Department of Defense (DoD) "does not sufficiently incorporate data into its acquisition-related learning and decision-making" and asked six questions about "the use of data analysis, measurement, and other evaluation-related methods in DoD acquisition programs." In this report, the authors decompose and measure acquisition functions, data governance, and training to assess how data and associated analytics support DoD acquisition decision making. The authors found that the DoD is applying a breadth of data analytics to acquisition. Capabilities range from simple data archives and plotting to archives integrated with commercial analytic tools. The DoD has implemented an array of data governance and management practices, but major challenges remain, including a culture against data sharing and concerns about security and oversight burden. Some commercial breakthroughs in advanced analytics sound promising for DoD acquisition, but some might not be applicable; research is ongoing. Advancement should include developing a data analytics strategy across acquisition domains, expanding data governance and data sharing, and continuing to expand and mature data collection, access, and analytic layers. Also, mechanisms are needed to authorize and ensure protected access to data for both the DoD and external analysts. Improved incentives and understanding of data analytics could encourage decision makers to make better use of capabilities" -- Publisher's description.