Software Engineering Research Center
Aditya Mathur, Purdue University
Director
Robert Cowan, West Virginia University
Assistant Director
An NSF Industry - University Cooperative Research Center
The SERC Organization - History of I/UCRCs and NSF mission
- Our Affiliates and the Administration
- Industry and University Partnerships
Current Research Projects Technology Transfer Examples Virtual SERC (VSERC)
The SERC Organization - History of I/UCRCs and NSF mission
- Our Affiliates and the Administration
- Industry and University Partnerships
Current Research Projects Technology Transfer Examples Virtual SERC (VSERC)
Outline for PresentationOutline for Presentation
National Science Foundation Promoting the Progress of Science
National Science Foundation Promoting the Progress of Science
Support research and education in science and engineering- Foster the interchange of scientific information among scientists and engineers in the United States and foreign countries
- Technology Transfer via industry/university partnerships
Support research and education in science and engineering- Foster the interchange of scientific information among scientists and engineers in the United States and foreign countries
- Technology Transfer via industry/university partnerships
Developed by NSF in 1970’s 50+ centers nation-wide - SERC: I/UCRC for Software Engineering
True partnership between industry and university researchers
Developed by NSF in 1970’s 50+ centers nation-wide - SERC: I/UCRC for Software Engineering
True partnership between industry and university researchers
Industry/University Cooperative Research Centers (IUCRCs)
Industry/University Cooperative Research Centers (IUCRCs)
The SERC IUCRCThe SERC IUCRC
Purdue University Ball State University
University of Florida University of West Florida
Oregon Associated Universities University of Oregon Portland State University Oregon State University Oregon Graduate Institute
West Virginia University
Purdue University Ball State University
University of Florida University of West Florida
Oregon Associated Universities University of Oregon Portland State University Oregon State University Oregon Graduate Institute
West Virginia University
Added in 1994
Established: 1986
Added in 1998
Current AffiliatesCurrent Affiliates
Army Research Lab: Atlanta,GA AveStar Inc.: Fairmont, WV Telcordia: Morristown, NJ British Telecom Labs: Martlesham Heath, England Dynamix Inc.: Eugene, OR ManTech: Fairmont, WV Motorola, Inc.: Schaumburg, IL NASA IV&V: Fairmont, WV Northern Telecom: Richardson, TX Northrop Grumman: Melbourne, FL Northrop Grumman: Rolling Meadows, IL Tivoli Systems, Inc: Indianapolis, IN
Army Research Lab: Atlanta,GA AveStar Inc.: Fairmont, WV Telcordia: Morristown, NJ British Telecom Labs: Martlesham Heath, England Dynamix Inc.: Eugene, OR ManTech: Fairmont, WV Motorola, Inc.: Schaumburg, IL NASA IV&V: Fairmont, WV Northern Telecom: Richardson, TX Northrop Grumman: Melbourne, FL Northrop Grumman: Rolling Meadows, IL Tivoli Systems, Inc: Indianapolis, IN
Examples of Research ProjectsExamples of Research Projects
Metrics,Tools for Measurement of Software Design Quality
Methods for Assessing and Assuring Data Quality Testing Distributed Systems Testing Video Games Evaluation of ROI on V&V Activities Software Process Improvement Modeling and Tools Computer-Based Instructional Technologies Test Coverage Analysis and Test Optimization Software Reuse
Metrics,Tools for Measurement of Software Design Quality
Methods for Assessing and Assuring Data Quality Testing Distributed Systems Testing Video Games Evaluation of ROI on V&V Activities Software Process Improvement Modeling and Tools Computer-Based Instructional Technologies Test Coverage Analysis and Test Optimization Software Reuse
Universities
Oregon Associated Schools
Purdue University Ball State University
University of Florida University of West Florida
West Virginia University
Industry and University NetworkIndustry and University Network
CompaniesArmy Research LabBellcoreBritish Telecom LabsDynamixManTechMotorola, Inc.NortelNorthrop Grumman, ILNorthrop Grumman, FLTivoli Systems, Inc., IN
Industrial Advisory Board Researchers
Technology and Software
Collaboration
Partnership
is composed of provide
produce
is available to
.
Industrial Advisory Board
Director: Aditya Mathur
Assistant Director: Robert Cowan
University of Oregon
Portland State University
Site Director: Stuart Faulk
Oregon State University
Site Director: Aditya Mathur
SERC Policy Board
SERC Administration
Site Director: Ali Mili
Oregon Graduate Institute
Site Director: Steve Thebaut
West Virginia University
Ball State University
Purdue University University of Florida
University of West Florida
National Science Foundation
Benefits to UniversitiesBenefits to Universities
Source of fresh research problems
access to industrially generated data and technology
fast turn-around on proposals
publishable research
satisfaction of seeing research used in industry
potential for additional funding from other NSF programs
Source of fresh research problems
access to industrially generated data and technology
fast turn-around on proposals
publishable research
satisfaction of seeing research used in industry
potential for additional funding from other NSF programs
Benefits to IndustryBenefits to Industry
Influence selection of research projects
exploit innovations that may lead to new products
improve software quality and productivity of software engineers
new software engineering technology….access to expertise
use of unique laboratories, facilities
satisfy corporate commitment to support university research
interaction with peers
technical reports
on-site short courses and seminars
summer interns, potential future employees
Influence selection of research projects
exploit innovations that may lead to new products
improve software quality and productivity of software engineers
new software engineering technology….access to expertise
use of unique laboratories, facilities
satisfy corporate commitment to support university research
interaction with peers
technical reports
on-site short courses and seminars
summer interns, potential future employees
SERC FundingSERC Funding
National Science Foundation Participating Universities Industrial Affiliates
- support faculty and graduate student projects
Membership Fees- $30,000 per year full membership
- $5,000 per year limited membership (for small businesses only)
National Science Foundation Participating Universities Industrial Affiliates
- support faculty and graduate student projects
Membership Fees- $30,000 per year full membership
- $5,000 per year limited membership (for small businesses only)
Semi-Annual SERC ShowcaseSemi-Annual SERC Showcase
Poster sessions Researcher presentations IAB member presentations Potential company presentations Software demonstrations IAB and SERC Admin. meetings
Poster sessions Researcher presentations IAB member presentations Potential company presentations Software demonstrations IAB and SERC Admin. meetings
.Industrial Request For Proposals: IRFPsIndustrial Request For Proposals: IRFPs
Army Research LabBellcoreBritish Telecom LabsDynamixMotorolaNortelNorthrop Grumman, ILNorthrop Grumman, FLTivoli Systems, Inc.
IAB SERC Staff
IRFPs
Broadcast IRFPs to SERC Universities
Purdue+
Florida+
Oregon+
West Virginia+
Broadcast Proposals from Researchers to Industrial Affiliates
interact with to generate
Software EvolutionJens Palsberg
Purdue University
Software EvolutionJens Palsberg
Purdue University
Problem: When requirements change, can a new software version be evolved automatically and efficiently?
Approach: Genetic programming.
Rationale: With genetic programming we can evolve programs by means of natural selection. To go from one software version to the next, it may be much faster to start with the previous version instead of starting from scratch.
Status: In our current genetic programming system, medium-sized image-processing software can be evolved from scratch.
Goals: Quantitative assessment of how well genetic programming can evolve new versions of software. Guidelines for conducting the evolution process. Study applications of the technology to SERC affiliate projects, beyond image processing.
Problem: When requirements change, can a new software version be evolved automatically and efficiently?
Approach: Genetic programming.
Rationale: With genetic programming we can evolve programs by means of natural selection. To go from one software version to the next, it may be much faster to start with the previous version instead of starting from scratch.
Status: In our current genetic programming system, medium-sized image-processing software can be evolved from scratch.
Goals: Quantitative assessment of how well genetic programming can evolve new versions of software. Guidelines for conducting the evolution process. Study applications of the technology to SERC affiliate projects, beyond image processing.
Data QualityAhmed Elmagarmid
Purdue University
Data QualityAhmed Elmagarmid
Purdue University
Problem: Approaching data quality in a more comprehensive way than what is currently being done. Existing scrubbing tools fail to realize that data is a complex entity that is replicated, dynamic, and tightly coupled to the business processes.
Approach: (a) Define a framework for data quality: Including dimensions, requirements, policies, and a taxonomy for dealing with and improving data quality, and, (b) Develop a methodology that deals properly with the issues of initial cleanup, continuous cleanup, and process improvements. The methodology must be comprehensive, implementable, and measurable.
Goals: Data quality has been a major cause of customer dissatisfaction, lost business, and inability to meet business objectives. While quality has a clearer meaning when it comes to software, it is much more poorly defined when it comes to data. A generally acceptable and operational definition of data quality refers to Accuracy, Consistency, Currency, and Completeness of data. The specific goal of this project is to develop a methodology that deals with all aspects of data quality.
Problem: Approaching data quality in a more comprehensive way than what is currently being done. Existing scrubbing tools fail to realize that data is a complex entity that is replicated, dynamic, and tightly coupled to the business processes.
Approach: (a) Define a framework for data quality: Including dimensions, requirements, policies, and a taxonomy for dealing with and improving data quality, and, (b) Develop a methodology that deals properly with the issues of initial cleanup, continuous cleanup, and process improvements. The methodology must be comprehensive, implementable, and measurable.
Goals: Data quality has been a major cause of customer dissatisfaction, lost business, and inability to meet business objectives. While quality has a clearer meaning when it comes to software, it is much more poorly defined when it comes to data. A generally acceptable and operational definition of data quality refers to Accuracy, Consistency, Currency, and Completeness of data. The specific goal of this project is to develop a methodology that deals with all aspects of data quality.
Software Maintenance:Spotlighting the Code Norman Wilde
University of West Florida
Software Maintenance:Spotlighting the Code Norman Wilde
University of West Florida
Description: “Where in this program is functionality X implemented?”
Method: Use test cases as probes to locate functionalities
Example: Where is call forwarding implemented? run small set of test cases that involve forwarding a call run set that does not involve the call analyze traces to look for program components that were executed in first
set and not second set
Goal: Spotlight code fragments Tech Transfer: to make Software Recon an affiliate productivity aid on
affiliate projects
Description: “Where in this program is functionality X implemented?”
Method: Use test cases as probes to locate functionalities
Example: Where is call forwarding implemented? run small set of test cases that involve forwarding a call run set that does not involve the call analyze traces to look for program components that were executed in first
set and not second set
Goal: Spotlight code fragments Tech Transfer: to make Software Recon an affiliate productivity aid on
affiliate projects
Tech Transfer Examples: Norman WildeTech Transfer Examples: Norman Wilde
BellcoreMaintenance of OO Programs
1991 - 1993
Bellcore concerned about the maintainability of OOPs, requested SERC research on this topic
Over two summers, UWF conducted interviews w/Bellcore programmers and performed study of OO tools in use at Bellcore
Study identified maintenance issues and tool needs for OOPs - earliest research on this topic
Results used in defining Bellcore’s programming environment Bellcore/UWF jointly authored papers in “IEEE Transactions on SE” and
“IEEE Software” UWF student hired by Bellcore to continue work
BellcoreMaintenance of OO Programs
1991 - 1993
Bellcore concerned about the maintainability of OOPs, requested SERC research on this topic
Over two summers, UWF conducted interviews w/Bellcore programmers and performed study of OO tools in use at Bellcore
Study identified maintenance issues and tool needs for OOPs - earliest research on this topic
Results used in defining Bellcore’s programming environment Bellcore/UWF jointly authored papers in “IEEE Transactions on SE” and
“IEEE Software” UWF student hired by Bellcore to continue work
Norman Wilde: Technology Transfer (cont.)Norman Wilde: Technology Transfer (cont.)
Northrop Grumman (RM)Software Recon tool
1995 - current
trial of Software Recon conducted at RM on a NG in-house C language code
management system
several tool improvements made based on feedback from NG programmers
C Software Recon tool now in use
NG request Ada Recon tool, now under develop.
Northrop Grumman (RM)Software Recon tool
1995 - current
trial of Software Recon conducted at RM on a NG in-house C language code
management system
several tool improvements made based on feedback from NG programmers
C Software Recon tool now in use
NG request Ada Recon tool, now under develop.
Norman Wilde: Technology Transfer (cont.)Norman Wilde: Technology Transfer (cont.)
Northrop Grumman (FL)Recovery of Design Threads
1995 - 1997
NG software engineers suggest Recon could be used to recover “design threads” from JSTARS, large battle management system
collaborate on a trial, which successfully recovered threads
joint authored paper to appear in “Journal of Systems and Software”
Northrop Grumman (FL)Recovery of Design Threads
1995 - 1997
NG software engineers suggest Recon could be used to recover “design threads” from JSTARS, large battle management system
collaborate on a trial, which successfully recovered threads
joint authored paper to appear in “Journal of Systems and Software”
Design Metrics Dolores and Wayne Zage
Ball State University
Design Metrics Dolores and Wayne Zage
Ball State University
Description: During the software design process, developers should be able to infer more about the software they are developing. Computing metrics allows one to choose the best design, as well as identify stress points that may lead to difficulty during coding and maintenance. (Currently developing OO design metrics.)
Method: Use metrics De and Di and D(G) to identify error-prone modules in a
software design. Use DB (design balance) and DC (design connectivity) to gain a better
understanding of a developing system. Various strategies have been employed by current affiliates to correct or
monitor troublesome modules.
Goal: Improve software development processes and products.
Description: During the software design process, developers should be able to infer more about the software they are developing. Computing metrics allows one to choose the best design, as well as identify stress points that may lead to difficulty during coding and maintenance. (Currently developing OO design metrics.)
Method: Use metrics De and Di and D(G) to identify error-prone modules in a
software design. Use DB (design balance) and DC (design connectivity) to gain a better
understanding of a developing system. Various strategies have been employed by current affiliates to correct or
monitor troublesome modules.
Goal: Improve software development processes and products.
Technology Transfer Example: ZagesTechnology Transfer Example: Zages
Bellcore, Northrop Grumman, Army Research Lab
NG started by using the metrics’ ability to predict error-prone modules in a missile defense system.
Since then, incorporated DM into software development methodology as NG strives to go from Level 3 to Level 4. Calculating the Zages’ metrics is included in the set of requirements for new projects.
DMAA and DMAC now in use at affiliate sites.
Bellcore, Northrop Grumman, Army Research Lab
NG started by using the metrics’ ability to predict error-prone modules in a missile defense system.
Since then, incorporated DM into software development methodology as NG strives to go from Level 3 to Level 4. Calculating the Zages’ metrics is included in the set of requirements for new projects.
DMAA and DMAC now in use at affiliate sites.
DM
Ball State Research Team
DMDMDM
Northrop Grumman
SEI Level 4
Technology Transfer Example: ZagesTechnology Transfer Example: Zages
Bellcore, Northrop Grumman, Army Research Lab
At Bellcore: DM have been excellent predictors of stress points in telecommunications software
DM technology moving from Bellcore research to Bellcore development groups
Bellcore and BSU joint papers
BSU DM team student interns at NGC and Bellcore in Summer 1997
Bellcore, Northrop Grumman, Army Research Lab
At Bellcore: DM have been excellent predictors of stress points in telecommunications software
DM technology moving from Bellcore research to Bellcore development groups
Bellcore and BSU joint papers
BSU DM team student interns at NGC and Bellcore in Summer 1997
Metrics Directed Verification of SDL Designs
Dolores and Wayne ZageBall State University
Metrics Directed Verification of SDL Designs
Dolores and Wayne ZageBall State University
Motorola
Goal: Apply DM technology to identify stress points in SDL designs and assess the utility and effectiveness of design metrics on such designs.
Method: Map DM primitives to SDL design artifacts. Calculate metrics and and determine if stress points are error-prone.
Motorola
Goal: Apply DM technology to identify stress points in SDL designs and assess the utility and effectiveness of design metrics on such designs.
Method: Map DM primitives to SDL design artifacts. Calculate metrics and and determine if stress points are error-prone.
Predicting the Performance of Software Process Improvements
David Raffo, Portland State University
Predicting the Performance of Software Process Improvements
David Raffo, Portland State University
Description: Previous research: Process Tradeoff Method (PTA) predicts the impact of process improvements on: development cost product quality task schedule
Current Project: Further develop these methods by applying the techniques to address a
specific question: What is the impact of implementing the Personal Software Process (PSP) in a real world development setting?
Goal: Accommodate new processes, process changes, and project environments Develop graphical and quantitative process models which can generally be
used to predict the impact of implementing CMM KPAs and other process changes
Description: Previous research: Process Tradeoff Method (PTA) predicts the impact of process improvements on: development cost product quality task schedule
Current Project: Further develop these methods by applying the techniques to address a
specific question: What is the impact of implementing the Personal Software Process (PSP) in a real world development setting?
Goal: Accommodate new processes, process changes, and project environments Develop graphical and quantitative process models which can generally be
used to predict the impact of implementing CMM KPAs and other process changes
Analysis of Software Process Models and Metrics: Moving to Levels 4 and 5 of the CMM
David Raffo & Warren HarrisonPortland State University
Analysis of Software Process Models and Metrics: Moving to Levels 4 and 5 of the CMM
David Raffo & Warren HarrisonPortland State University
Description: Develop innovative application of TQM principles to software project management using software process models and metrics.
Approach: Develop a suite of software process and product metrics along with software process models which together can be used in an integrated framework to support project management and process planning decisions.
Goal: Develop a systematic approach to help companies achieve the Quantitative Process Management, Software Quality Management, and the Continuous Process Improvement KPAs of the CMM.
Description: Develop innovative application of TQM principles to software project management using software process models and metrics.
Approach: Develop a suite of software process and product metrics along with software process models which together can be used in an integrated framework to support project management and process planning decisions.
Goal: Develop a systematic approach to help companies achieve the Quantitative Process Management, Software Quality Management, and the Continuous Process Improvement KPAs of the CMM.
Estimating Software Reliability During Conceptual DesignBruce D’Ambrosio
Oregon State University
Estimating Software Reliability During Conceptual DesignBruce D’Ambrosio
Oregon State University
Description: Traditional reliability estimation methods depend on data from product testing. However, many software technologies are evolving so rapidly that failure rates of existing systems may not provide a good indication of reliability of systems under design and development, even in the early, conceptual design phase.
Approach: Develop models of software reliability based on expert judgement
combined with statistical information. Exploit advances in Bayesian probability theory to develop software reliability models which can combine expert estimates of factors influencing reliability with statistical information.
Goal: Develop methods and tools for predicting the reliability of complex
software systems during early design.
Description: Traditional reliability estimation methods depend on data from product testing. However, many software technologies are evolving so rapidly that failure rates of existing systems may not provide a good indication of reliability of systems under design and development, even in the early, conceptual design phase.
Approach: Develop models of software reliability based on expert judgement
combined with statistical information. Exploit advances in Bayesian probability theory to develop software reliability models which can combine expert estimates of factors influencing reliability with statistical information.
Goal: Develop methods and tools for predicting the reliability of complex
software systems during early design.
Computer-aided Testing for Reusable Software ComponentsMark Yang
University of Florida
Computer-aided Testing for Reusable Software ComponentsMark Yang
University of Florida
Description: Reuse has been widely discussed and promoted in the software industry, but many aspects of current practice has to be modified in order for reuse to be practical. For example, the change of reliability when a component is used in a new environment has not been well investigated. Without some guidelines, we may not have the confidence to reuse a component. How should the component be re-tested, and if necessary be modified?
(cont.)
Description: Reuse has been widely discussed and promoted in the software industry, but many aspects of current practice has to be modified in order for reuse to be practical. For example, the change of reliability when a component is used in a new environment has not been well investigated. Without some guidelines, we may not have the confidence to reuse a component. How should the component be re-tested, and if necessary be modified?
(cont.)
Computer-aided Testing for Reusable Software Components(cont.)
Mark YangUniversity of Florida
Computer-aided Testing for Reusable Software Components(cont.)
Mark YangUniversity of Florida
Approach: To study reliability estimation methods under a changing environment To figure out the number of new test cases needed in order to meet the
reliability requirement in a new environment To build a tool that takes the old testing results, usage records, new
environments, and new reliability requirement as inputs and outputs the number of new test cases
To assess software reliability estimation methods from the reuse point of view. Which one is the best choice for reuse?
Goal: To build a tool that can certify reusable components based on past testing history and present requirements
Approach: To study reliability estimation methods under a changing environment To figure out the number of new test cases needed in order to meet the
reliability requirement in a new environment To build a tool that takes the old testing results, usage records, new
environments, and new reliability requirement as inputs and outputs the number of new test cases
To assess software reliability estimation methods from the reuse point of view. Which one is the best choice for reuse?
Goal: To build a tool that can certify reusable components based on past testing history and present requirements
Testing Distributed Systems
Aditya Mathur and Sudipto Ghosh Purdue University
Testing Distributed Systems
Aditya Mathur and Sudipto Ghosh Purdue University
Description: Testing distributed systems composed of software components for tolerance
to component failures, security and correctness of implementation. Developing interface based fault injection methods between components and
metrics in order to assess the adequacy of testing.
Method: Identify the source of errors and failures in a distributed system Develop and validate metrics to assess test adequacy Study the behavior of system under component failure
(cont.)
Description: Testing distributed systems composed of software components for tolerance
to component failures, security and correctness of implementation. Developing interface based fault injection methods between components and
metrics in order to assess the adequacy of testing.
Method: Identify the source of errors and failures in a distributed system Develop and validate metrics to assess test adequacy Study the behavior of system under component failure
(cont.)
Goal:
Develop a methodology to assess the tolerance of a distributed system to failures occurring in one or more of its components
Develop and assess software fault injection testing methodology for distributed systems
Develop metrics for the assessment of adequacy of tests of distributed systems
Develop a prototype assessment tool for experimentation
Goal:
Develop a methodology to assess the tolerance of a distributed system to failures occurring in one or more of its components
Develop and assess software fault injection testing methodology for distributed systems
Develop metrics for the assessment of adequacy of tests of distributed systems
Develop a prototype assessment tool for experimentation
Testing Distributed Systems (cont.)
Aditya Mathur and Sudipto Ghosh Purdue University
Testing Distributed Systems (cont.)
Aditya Mathur and Sudipto Ghosh Purdue University
Bellcore
Fault Injection Testing
1994-1997 Bellcore explored the possibility of using fault injection testing
technology for software systems Study identified research issues involved in fault injection testing
and tool design for fault injection testing Over 3 years Purdue and Bellcore conducted studies on the
application of the fault injection testing technique on Bellcore’s systems
Bellcore/Purdue jointly authored paper in Quality Week 97, Europe
Purdue student hired as a summer intern by Bellcore
Bellcore
Fault Injection Testing
1994-1997 Bellcore explored the possibility of using fault injection testing
technology for software systems Study identified research issues involved in fault injection testing
and tool design for fault injection testing Over 3 years Purdue and Bellcore conducted studies on the
application of the fault injection testing technique on Bellcore’s systems
Bellcore/Purdue jointly authored paper in Quality Week 97, Europe
Purdue student hired as a summer intern by Bellcore
Technology Transfer Examples: Aditya Mathur and Sudipto Ghosh
Technology Transfer Examples: Aditya Mathur and Sudipto Ghosh
Description:
Traditional methods for software reliability assessment treat a software system as a “whole” and are applicable very late in the development cycle
We have proposed CBRE (Component-Based Reliability Estimation) to overcome the problems associated with traditional methods.
CBRE can also be applied to obtain early estimates of system reliability, i.e., prior to code availability.
The current focus is on comparing CBRE with Laprie-Kanoun method of component-based
reliability estimation evolving models of inter- and intra-component dependencies, determining ways of applying CBRE in early stages of development.
(cont.)
Description:
Traditional methods for software reliability assessment treat a software system as a “whole” and are applicable very late in the development cycle
We have proposed CBRE (Component-Based Reliability Estimation) to overcome the problems associated with traditional methods.
CBRE can also be applied to obtain early estimates of system reliability, i.e., prior to code availability.
The current focus is on comparing CBRE with Laprie-Kanoun method of component-based
reliability estimation evolving models of inter- and intra-component dependencies, determining ways of applying CBRE in early stages of development.
(cont.)
Architecture Based Estimation of Software Reliability Aditya Mathur, Pietro Michielan, Manuela Schiona
Purdue University
Architecture Based Estimation of Software Reliability Aditya Mathur, Pietro Michielan, Manuela Schiona
Purdue University
Collaboration:
This project is supported in part by a tie-project grant from the NSF. The tie is with Professor Kishor Trivedi from the Center for Advanced Communications at Duke University.
Other collaborators include: Dr. Jose Maldonado, University of Sao Paulo, Brazil. Professor Nozer Singpurwalla, George Washington University Dr. Alberto Pasquini of ENEA, Rome, Italy
Collaboration:
This project is supported in part by a tie-project grant from the NSF. The tie is with Professor Kishor Trivedi from the Center for Advanced Communications at Duke University.
Other collaborators include: Dr. Jose Maldonado, University of Sao Paulo, Brazil. Professor Nozer Singpurwalla, George Washington University Dr. Alberto Pasquini of ENEA, Rome, Italy
Architecture Based Estimation of Software Reliability(cont.)
Aditya Mathur, Pietro Michielan, Manuela SchionaPurdue University
Architecture Based Estimation of Software Reliability(cont.)
Aditya Mathur, Pietro Michielan, Manuela SchionaPurdue University
“Endless Frontier, Limited Resources”*“Endless Frontier, Limited Resources”*
Industry must increase its contribution to the U.S. R&D enterprise.
Industry must overcome private sector barriers to partnerships.
Industrial R&D must grow.
Industry must focus its research priorities. Industry must take timely advantage of the leading-edge results
coming out of U.S. universities and government labs.
*http://nii.nist.gov/pubs/coc_rd/rd_cover.html
Industry must increase its contribution to the U.S. R&D enterprise.
Industry must overcome private sector barriers to partnerships.
Industrial R&D must grow.
Industry must focus its research priorities. Industry must take timely advantage of the leading-edge results
coming out of U.S. universities and government labs.
*http://nii.nist.gov/pubs/coc_rd/rd_cover.html
Educational ActivitiesEducational Activities
Re-training Courses
10-week, 400-hour hand-on course in Software Engineering offered to 30
employees of Raytheon during summer of 1998.
Specialized Course Modules
1-week modules in Design, Testing, WEB-site Construction and Management,
Java, and other languages.
Master in Software Engineering
Oregon Associated Universities (http://www.omse.org/)
Re-training Courses
10-week, 400-hour hand-on course in Software Engineering offered to 30
employees of Raytheon during summer of 1998.
Specialized Course Modules
1-week modules in Design, Testing, WEB-site Construction and Management,
Java, and other languages.
Master in Software Engineering
Oregon Associated Universities (http://www.omse.org/)