Aug 6
2012
By Robert Stoddard,
Senior Member of the Technical Staff
Software Engineering Measurement and Analysis Program
As
part of our research related to early acquisition lifecycle cost
estimation for the Department of Defense (DoD), my colleagues in the
SEI’s Software Engineering Measurement & Analysis initiative
and I began envisioning a potential solution that would rely heavily on
expert judgment of future possible program execution scenarios.
Previous to our work on cost estimation, many parametric cost models
required domain expert input, but, in our opinion, they did not address
alternative scenarios of execution that might occur from Milestone A onward. Our approach, known as Quantifying Uncertainty in Early Lifecycle Cost Estimation (QUELCE),
asks domain experts to provide judgment not only on uncertain cost
factors for a nominal program execution scenario, but also for the
drivers of cost factors across a set of anticipated scenarios. This blog
post describes our efforts to improve the accuracy and reliability of
expert judgment within this expanded role of early lifecycle cost estimation.
Read more...
Jun 11
2012
By Dave Zubrow,
Chief Scientist
Software Engineering Process Management Program
By law, major defense acquisition programs are now required to prepare cost estimates earlier in the acquisition lifecycle, including pre-Milestone A,
well before concrete technical information is available on the program
being developed. Estimates are therefore often based on a desired
capability—or even on an abstract concept—rather than a concrete
technical solution plan to achieve the desired capability. Hence the
role and modeling of assumptions becomes more challenging. This blog
posting outlines a multi-year project on Quantifying Uncertainty in Early Lifecycle Cost Estimation (QUELCE) conducted by the SEI Software Engineering Measurement and Analysis (SEMA)
team. QUELCE is a method for improving pre-Milestone A software cost
estimates through research designed to improve judgment regarding
uncertainty in key assumptions (which we term program change drivers), the relationships among the program change drivers, and their impact on cost.
Read more...
Feb 20
2012
By Dave Zubrow, Manager
Software Engineering Measurement and Analysis Initiative
The SEI has been actively engaged in defining and studying high maturity software engineering practices for several years. Levels 4 and 5 of the CMMI (Capability Maturity Model Integration)
are considered high maturity and are predominantly characterized by
quantitative improvement. This blog posting briefly discusses high
maturity and highlights several recent works in the area of high
maturity measurement and analysis, motivated in part by a recent comment on a Jan. 30 post
asking about the latest research in this area. I’ve also included links
where the published research can be accessed on the SEI website.
Read more...
Dec 19
2011
Acquisition , Acquisition Dynamics , Agile , Architecture Documentation , Attribute Driven Design (ADD) , Binaries , Cyber-physical Systems , Fuzzy Hashing , Handheld Devices , Malware , Measurement & Analysis , Resilience Management Model (RMM) , Safety-Related Requirements , Security-Related Requirements , SEI Research , Software Cost Estimates , Team Software Process (TSP) , Technical Debt
By Douglas C. Schmidt
Chief Technology Officer
A key mission of the SEI is to advance the practice of software engineering and cyber security through research and technology transition
to ensure the development and operation of software-reliant Department
of Defense (DoD) systems with predictable and improved quality,
schedule, and cost. To achieve this mission, the SEI conducts research
and development (R&D) activities involving the DoD, federal
agencies, industry, and academia. One of my initial blog postings
summarized the new and upcoming R&D activities
we had planned for 2011. Now that the year is nearly over, this blog
posting presents some of the many R&D accomplishments we completed
in 2011.
Read more...
Dec 12
2011
By Dennis R. Goldenson
Senior Member of the Technical Staff
Software Engineering Measurement and Analysis
As
with any new initiative or tool requiring significant investment, the
business value of statistically-based predictive models must be
demonstrated before they will see widespread adoption. The SEI Software Engineering Measurement and Analysis (SEMA)
initiative has been leading research to better understand how existing
analytical and statistical methods can be used successfully and how to
determine the value of these methods once they have been applied to the
engineering of large-scale software-reliant systems. As part of this
effort, the SEI hosted a series of workshops that brought together
leaders in the application of measurement and analytical methods in many
areas of software and systems engineering. The workshops help identify
the technical barriers organizations face when they use advanced
measurement and analytical techniques, such as computer modeling and
simulation. This post focuses on the technical characteristics and
quantified results of models used by organizations at the workshops.
Read more...
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