A Technical History of the SEI

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Measurement and Analysis The Challenge: Measuring Software Development Capabilities and Products Measurement and analysis in software engineering has long been a topic of interest. Without it, there is no clear, quantitative picture of software development capabilities or a basis for predicting or comparing products and processes. Although early software projects used several systems for predicting software costs, measurement was done in different ways and was often based on definitions that were inconsistent. The need for a standard and reliable set of measures that would help acquisition program managers and software development contractors alike was an early priority for the SEI. A Solution: Approaches for Collecting and Analyzing Data In response to the DoD’s 1991 Software Technology Strategy, the SEI agreed to lead the development of a set of core measures to “help the DoD plan, monitor, and manage its internal and contracted software development projects” [Carleton 1992]. In collaboration with measurement experts, including those who developed prediction systems, the SEI developed definition frameworks for a set of core measures. The measures focus on size, defects, effort, and schedule. These definition frameworks make it possible for organizations to use the measures that best match their processes and infrastructure while benefiting from a standard way of describing the operational definitions in detail. Once the definition problem for measures was resolved, the SEI turned its attention to helping organizations decide what to measure. Experience from management information systems shows that many reports could be generated that have little to no impact on decision making within the organization. The SEI began to investigate the goal-question-metric method for aligning measurement with information needs in the organization [Basili 1984]. The SEI modified this approach to include explicit consideration of the output of the measurement activity—that is, the indicator to be used by decision makers. The method was dubbed goal-question-indicator-measure (GQ(I)M). Using the GQ(I)M approach helps mitigate the risk of measuring and reporting information that provides little or no value to the organization. As approaches for conducting measurement effectively matured and were disseminated, the next significant challenge became data analysis [Paulk 2000]. The notion of analysis, especially analysis related to process improvement, was often equated with the high-maturity practices of the SW CMM and CMMI. More focus on the “analysis” part of measurement and analysis was also spurred by the establishment of the Measurement and Analysis process area in the CMMI [CPT 2002]. An early and foundational work in this area was Measuring the Software Process, which showed how statistical process control techniques could be fruitfully applied to software data [Florac 1999]. This was followed by substantial work on the application of Six Sigma analytical techniques to software engineering [Penn 2007]. The Six Sigma connection provided a rich set of tools as well as a “brand” that already had roots in many organizations, facilitating its adoption. The current work on developing estimates early in the DoD acquisition lifecycle incorporates CMU/SEI-2016-SR-027 | SOFTWARE ENGINEERING INSTITUTE | CARNEGIE MELLON UNIVERSITY 149 Distribution Statement A: Approved for Public Release; Distribution is Unlimited.
techniques used in Six Sigma, such as Bayesian Belief Networks, matrix transformation, and subjective input calibration methods. The method quantifies uncertainties, allows subjective inputs, visually depicts influential relationships among program change drivers and outputs, and assists with the explicit description and documentation underlying an estimate. The Consequence: Effective, Quantitative Basis for Improvement The SEI work in measurement and analysis has had far-reaching impact. The core measures report became a foundational work and is referenced by many subsequent publications on software measurement, and the definition checklists have been widely incorporated as part of the approach for specifying measures for cost estimation. SEI-developed training courses on analyzing software data help others leverage the power of statistics in understanding and gaining insight from the measures collected about software projects, processes, and products. The SEI Contribution The SEI did not work alone in its attempt to move the measurement and analysis community forward. One specific application of the early measurement definition work was in conjunction with the Cost Constructive Cost Model (CoCoMo) [Boehm 2000]. A primary input to cost models is an estimate of the size of the software to be built. The CoCoMo model uses the SEI’s size definition checklist approach to specify the operational definition of the number of lines of code to be developed. Also, much work, especially in the early years, was done in collaboration with the Practical Software Measurement (PSM) initiative, sponsored by the DoD and the U.S. Army [PSM 2012]. While there are some differences in methods and techniques, the underlying principles in SEI and PSM measurement and analysis products are virtually the same. The SEI continues to participate in the PSM’s active, collaborative forum for measurement and analysis. The SEI has consistently conducted research and development related to measurement and analysis. While the initial focus was on helping to identify and standardize measures related to project management and process improvement, it grew to include the application of quantitative analytical techniques for use of the data. Looking to the future, the SEI is developing a research agenda to investigate the application of probabilistic and modeling techniques in measurement and analysis. References [Basili 1984] Basili, V. & Weiss, D. “A Methodology for Collecting Valid Software Engineering Data.” IEEE Transactions on Software Engineering 10, 3 (November 1984): 728-738. [Boehm 2000] Boehm, Barry W.; Abts, Chris; Brown, Winsor A.; Chulani, Sunita, et al. Software Cost Estimation With Cocomo II. Prentice Hall, 2000 (ISBN 0130266922). [Carleton 1992] Carleton, Anita; Park, Robert; Bailey, Elizabeth; Goethert, Wolfhart; Florac, William; & Pfleeger, Shari. Software Measurement for DoD Systems: Recommendations for Initial Core Measures (CMU/SEI-92-TR-019). Software Engineering Institute, Carnegie Mellon University, 1992. http://resources.sei.cmu.edu/library/asset-view.cfm?AssetID=11675 [CPT 2002] CMMI Product Team. CMMI for Software Engineering, Version 1.1, Staged Representation (CMMI-SW, V1.1, Staged) (CMU/SEI-2002-TR-029). Software Engineering Institute, CMU/SEI-2016-SR-027 | SOFTWARE ENGINEERING INSTITUTE | CARNEGIE MELLON UNIVERSITY 150 Distribution Statement A: Approved for Public Release; Distribution is Unlimited.
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A Technical History of the SEI Larr
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Table of Contents Foreword Preface
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The Consequence: Undergraduate Soft
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References 131 Expanding the CMMI P
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The Challenge: Configuration Suppor
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References 275 Operational Support
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Foreword Angel Jordan University Pr
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to the SEI on a part-time basis as
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Blaise Durante Deputy Assistant Sec
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synergy between the TRA team’s ob
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Preface This report chronicles the
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Abstract This report chronicles the
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1 Introduction CMU/SEI-2016-SR-027
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Introduction In December 1984, the
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promulgate use of modern techniques
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Interpreting the Charter In creatin
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of Defense and flag officers from t
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(CRADA) funding is available to exp
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Another effective mechanism has bee
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2 Real-Time Embedded Systems Engine
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Figure 3: Real-Time Embedded System
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Introduction to Real-Time Embedded
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in those developments. The SEI was,
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general-purpose techniques but with
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Summary DoD systems have traditiona
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Ada/Real-Time Embedded Systems Test
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enchmark. Results of testbed experi
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Distributed Ada Real-Time Kernel Th
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Ada Adoption Handbook The Challenge
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[Hefley 1992] Hefley, William; Fore
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processor utilization. The sample a
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References [AFSAB 1988] Air Force S
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also known as using simplicity to c
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Software for Heterogeneous Machines
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[Barbacci 1986b] Barbacci, Mario &
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The increase in processing capacity
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[Lakshmanan 2011] Lakshmanan, Karth
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analysis as well as model tuning fo
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References [Hansen 2004] Hansen, J.
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or asynchronous networked computer
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3 Education and Training CMU/SEI-20
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Figure 4: Education and Training Ti
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making materials available to allow
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course offering, while at other uni
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[DoD 1984] Statement of Work for Im
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The curriculum guidelines also sugg
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Undergraduate Software Engineering
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SEI adapted the Personal Software P
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Software Assurance Curriculum for C
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how they relate to business needs.
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Survivability and Information Assur
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The SEI curriculum and others’ co
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The CSEE conference series also bro
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CMU Master of Software Engineering
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Executive Education Program The Cha
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Professional Training The Challenge
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Education and Training Delivery Pla
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[Ardis 2005] Ardis, Mark. “An Inc
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practical for keeping content curre
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[Johnson 2012] Johnson, Col. Rivers
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4 Management CMU/SEI-2016-SR-027 |
Page 125: Figure 5: Management Timeline CMU/S
Page 128 and 129: Introduction to Management When the
Page 130 and 131: neering CMM [SEI 1995] to describe
Page 132 and 133: under SEI stewardship in 2009. Deve
Page 134 and 135: References [Averill 1993] Averill,
Page 136 and 137: [SEI 1995] A Systems Engineering Ca
Page 138 and 139: The SEI developed a more explicit m
Page 140 and 141: [Humphrey 1988] Humphrey, Watts; Sw
Page 142 and 143: opinion leaders and senior managers
Page 144 and 145: [Hayes 1995] Hayes, William & Zubro
Page 146 and 147: move from maturity level to maturit
Page 148 and 149: The People Capability Maturity Mode
Page 150 and 151: The SEI Contribution As is the case
Page 152 and 153: and security process improvement. T
Page 154 and 155: [Caralli 2004] Caralli, Richard. Ma
Page 156 and 157: 4.6.2.1 CMMI Constellations For CMM
Page 158 and 159: ity and widespread use of the CMMI
Page 160 and 161: Expanding the CMMI Product Suite to
Page 162 and 163: key leaders within that organizatio
Page 164 and 165: The Smart Grid The Challenge: The N
Page 166 and 167: ecoming more widespread. As utiliti
Page 168 and 169: Continuous Risk Management training
Page 170 and 171: [Alberts 2009] Alberts, Christopher
Page 172 and 173: The principal motivator for the dev
Page 174 and 175: References [Carleton 2010] Carleton
Page 178 and 179: Carnegie Mellon University, 2002. h
Page 180 and 181: activities. The final set of activi
Page 182 and 183: [Rombach 1989] Rombach, H. Dieter &
Page 184 and 185: 5 Security CMU/SEI-2016-SR-027 | SO
Page 186: Figure 6: Security Timeline CMU/SEI
Page 189 and 190: Introduction to Security In the ear
Page 191 and 192: The CERT/CC helps guard against dev
Page 193 and 194: The SEI concern about risks posed t
Page 195 and 196: Teams (FIRST), which was formed in
Page 197 and 198: [Killcrece 2008] Killcrece, Georgia
Page 199 and 200: The CERT/CC built on its vast colle
Page 201 and 202: Malicious Code Analysis The Challen
Page 203 and 204: have put advanced tools in the hand
Page 205 and 206: Secure Coding The Challenge: Preven
Page 207 and 208: end, the SEI has developed training
Page 209 and 210: Network Situational Awareness The C
Page 211 and 212: In 2007, the Comprehensive National
Page 213 and 214: study. In 2011, the DoD began fundi
Page 215 and 216: [CSO 2004] CSO Magazine, in coopera
Page 217 and 218: People who work in small organizati
Page 219 and 220: [Violino 2010] Violino, Bob. “IT
Page 221 and 222: Since its initial development in 20
Page 223 and 224: References [Alberts 2005] Alberts,
Page 225 and 226: 6 Software Engineering Methods CMU/
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Introduction to Software Engineerin
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were delivered to Army and Air Forc
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Configuration Management The Challe
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The SEI Contribution The SEI recogn
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CASE Environments The Challenge: Ma
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The SEI Contribution The CASE work
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Software Technology Reference Guide
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Reengineering The Challenge: Legacy
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[Bergey 1999] Bergey, John; Smith,
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2. Influence is more important than
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[Brownsword 2004] Brownsword, Lisa;
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Developing Systems with Commercial
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The SEI Contribution When the SEI s
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[Tyson 2003] Tyson, Barbara; Albert
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system is safe); (2) evidence suppo
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7 Architecture CMU/SEI-2016-SR-027
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1984 1986 1988 1990 1992 1994 1996
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Introduction to Software Architectu
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cation description language, in the
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AFSC/ESD conducted a source selecti
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[Brown 1995] Brown, Alan; Carney, D
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Structural Modeling The Challenge:
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The SEI Contribution As a result of
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Federal Aviation Administration Stu
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evolving understanding of software
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The basic features of Serpent were
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Software Architecture Analysis Meth
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A survey of software architecture e
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Quality Attributes The Challenge: M
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[Barbacci 1995] Barbacci, Mario; Kl
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The Consequence: Effective Evaluati
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[Gallagher 2000] Gallagher, Brian.
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design with some variation built in
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Software Product Lines The Challeng
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A software product line curriculum.
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8 Forensics CMU/SEI-2016-SR-027 | S
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Figure 8: Forensics Timeline CMU/SE
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Introduction to Computer Forensics:
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ultimate contribution of SEI digita
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The SEI’s experience with cases s
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Digital Intelligence and Investigat
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that not only apply to the specific
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9 The Future of Software Engineerin
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A Vision of the Future of Software
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In each case, impact is achieved th
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e-estimation as a means to assess p
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and complexity of these systems are
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10 Conclusion CMU/SEI-2016-SR-027 |
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Highlighting 30 Years of Contributi
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developed network situational aware
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Direct Support to Government System
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In the course of these reviews, the
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Appendix Authors Contributing to th
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REPORT DOCUMENTATION PAGE Form Appr
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A Technical History of the SEI

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