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/)