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(The University of Southern California)

The University of Southern California (commonly called USC and other names) is a private, nonsectarian, research university located in the University Park neighborhood in Los Angeles, California, USA. USC was founded in 1880, making it California's oldest private research university.

More information: http://en.wikipedia.org/wiki/University_of_Southern_California

Acquisition and the ICM CSSE ARR, March 2008 Bruce Amato, Barry Boehm, Les DeLong, Gary Hafen, Adrian Pitman, Don Reifer, Peter Suk, Denton Tarbet, Rich Turner

Acquisition and the ICM CSSE ARR, March 2008 Bruce Amato, Barry Boehm, Les DeLong, Gary Hafen, Adrian Pitman, Don Reifer, Peter Suk, Denton Tarbet, Rich Turner
Integrating Systems & Software Architecting Workshop

Integrating Systems & Software Architecting Workshop

Participants Problem space examples Followed JAL approach: two full pages of (20) issues All Issues, refactored as appropriate Issues with an incomplete list of proposed solutions Bottom line summary of Issues

Systems Engineering and Project Management: Similarities and Differences for Cost Estimation Dr. Ricardo Valerdi – Massachusetts Institute of Technology Leone Young – Stevens Institute of Technology COSYSMO Workshop The University of Southern California (USC) November 4th, 2010 The 25th Int’l Forum on COCOMO & Systems/Software Cost Modeling *

11/03/09 * Integrating Cost and Schedule Estimation In The COSYSMOR Model/Tool John E. Gaffney, Jr. 301-721-5710 j.gaffney@lmco.com Center For Process Improvement Excellence Corporate Engineering and Technology Lockheed Martin Corporation November 3, 2009 (c) Copyright Lockheed Martin Corporation 2009
An Energy Consumption Framework for Distributed Java-Based Systems Chiyoung Seo Software Architecture Research Group University of Southern California

An Energy Consumption Framework for Distributed Java-Based Systems Chiyoung Seo Software Architecture Research Group University of Southern California
An Analysis of Changes in Productivity and COCOMO Cost Drivers over the Years Vu Nguyen, Barry Boehm Center for Systems and Software Engineering (CSSE) 24th Int’l COCOMO Forum Nov 3rd, 2009

An Analysis of Changes in Productivity and COCOMO Cost Drivers over the Years Vu Nguyen, Barry Boehm Center for Systems and Software Engineering (CSSE) 24th Int’l COCOMO Forum Nov 3rd, 2009
Melodic Segmentation: Evaluating the Performance of Algorithms and Musical Experts Belinda Thom, Christian Spevak, Karin Höthker Institut für Logik, Komplexität und Deduktionssystene, Universität Karlsruhe

Melodic Segmentation: Evaluating the Performance of Algorithms and Musical Experts Belinda Thom, Christian Spevak, Karin Höthker Institut für Logik, Komplexität und Deduktionssystene, Universität Karlsruhe

Azad M. Madni & Colin J. Neill SET Team January 28, 2010
An Analytical Comparison between Pair Development and Software Development with Inspection By: Monvorath (Molly) Phongpaibul phongpai@usc.edu Present to: USC- CSSE- ARR 2007 February 13, 2007

An Analytical Comparison between Pair Development and Software Development with Inspection By: Monvorath (Molly) Phongpaibul phongpai@usc.edu Present to: USC- CSSE- ARR 2007 February 13, 2007
UIR Alert Agent : An alert system for identifying suspicious web-site browsing leading to unintended information revelation(UIR) Rohini K. Srihari State University of New York at Buffalo May 6, 2003

UIR Alert Agent : An alert system for identifying suspicious web-site browsing leading to unintended information revelation(UIR) Rohini K. Srihari State University of New York at Buffalo May 6, 2003
Building Models from Your Software Data Brad Clark, Ph.D. Software Metrics, Inc. 16th International Forum on COCOMO and Software Cost Modeling Los Angeles, CA October 23-26, 2001

Building Models from Your Software Data Brad Clark, Ph.D. Software Metrics, Inc. 16th International Forum on COCOMO and Software Cost Modeling Los Angeles, CA October 23-26, 2001

New System Dynamics Models Ray Madachy madachy@usc.edu USC-CSE Annual Research Review March 11, 2002

Constructive Quality Model – Orthogonal Defect Classification (COQUALMO-ODC) Model Keun Lee ( keunlee@sunset.usc.edu) & Sunita Chulani (Sunita_Chulani@us.ibm.com)
A Framework for the Assessment and Selection of Software Components and Connectors in COTS-based Architectures Jesal Bhuta, Chris Mattmann {jesal, mattmann}@usc.edu USC Center for Systems & Software Engineering http://csse.usc.edu February 13, 2007

A Framework for the Assessment and Selection of Software Components and Connectors in COTS-based Architectures Jesal Bhuta, Chris Mattmann {jesal, mattmann}@usc.edu USC Center for Systems & Software Engineering http://csse.usc.edu February 13, 2007

COCOTS Life Cycle Estimation: Some Preliminary Observations Chris Abts Betsy Clark USC Center for Software Engineering Software Metrics Inc. October 25, 2001 Copyright 2001 University of Southern California

Ideal University Culture USC Current Description Does Not Fit USC Collegial/Supportive (20) Collegial/Supportive (18) Corporate Innovative/Creative/ Curious (10) Innovative/Creative/ Curious (15) Cosmopolitan Intellectual/Scholarly (5) Intellectual/Scholarly (11) Aspirational Exceptional (7) Real World (6) Exploration Real World (6) Exceptional (5) Radical Faculty Role (3) Transparent Ethical (3) Preserving and transmitting cultural heritage USC CULTURE THEMES FALL 2010 WITH MENTIONS IN PARENTHESES
Future Combat Systems SOS Characteristics and Critical Success Factors Edgar Dalrymple Associate Director, Software and Distributed Systems October, 2006

Future Combat Systems SOS Characteristics and Critical Success Factors Edgar Dalrymple Associate Director, Software and Distributed Systems October, 2006

Software Model Clashes Identification USC-CSE Annual Research Review Mohammed Al-Said
Review of material from previous week: Variance

Review of material from previous week: Variance

Formula for sample variance To find the variance and its square root, the standard deviation, use the command Analyze/ Descriptive Statistics/ Frequencies and move the variable of interest into the Variables box. Click on Statistics, check standard deviation and variance and then click OK. standard deviation--(s or SD) The variance (s2) is the sum of the squared deviations from the sample mean, divided by N-1 where N is the number of cases. (Note: in the formula for the population variance, the denominator is N rather than N-1)
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