Transcript Short status report on the CeBASE experience repository: towards a

USC
C S E
University of Southern California
Center for Software Engineering
CeBASE Experience Base (eBASE)
-Shared Vision
Barry Boehm, Vic Basili, and CeBASE Team
ISERN Presentation
August 2001
August 2001
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USC
C S E
University of Southern California
Center for Software Engineering
System Capability Description
• Provide better access to empirical data and
models
– And better understanding of their relevance
• Provide Web portal to categorized software
engineering data, models, and methods
– More extensive
– Better categorized
– Easier to use and contribute to
August 2001
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USC
C S E
University of Southern California
Center for Software Engineering
CeBASE Results Chain
Populate KB in
COTS-Based Systems (CBS)
Defect Reduction (DR) areas
Collaborative
empirical research
in CBS, DR
Empirical
KB content
Construct
knowledgebased (KB)
framework
Integrate
UMD, USC
data, models
Develop
CeBASE
portal
KB form
Growing
KB of empirical
best practices
models in
CBS, DR areas
Initial
Empirical KB
in CBS, DR
areas
Compatible
data, models
Access
facilitation
New
results
Initial KB,
empirical
techniques
Growing KB,
empirical
techniques
Wide, easy access to CeBASE KB :
Empirical best practices, models, data
Empirical research tools and techniques
more users
Continued
funding
Expand to other
collaborators, areas,
best practices
Expanded
results
Growing
community of
CeBASE users,
empirical
researchers;
KB content
CeBASE usage
cost-benefit
justification
Outreach and technology transition activities:
Contact, awareness, understanding, trial use, limited adoption, institutionalization
Much more
predictable
development
of both rapid and
robust
software
Critical-mass
community of
empirical SW
researchers;
scientific
understanding
of software
phenomenology
USC
C S E
University of Southern California
Center for Software Engineering
Key Stakeholders and Win Conditions
• Users: practitioners, empirical researchers
– Easy, rapid, dependable, low-cost operation
– Search, navigate, download, contribute
– Metadata for understanding data context
• Customer: NSF
– Scientific and practical value
• Developers and Maintainers: CeBASE team
– Strong IOC by Spring 2002; easy to run and evolve
• Interoperators: International, DACS, others
– Compatible data definitions
– Data cross-link and aggregation
August 2001
©USC-CSE
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USC
C S E
University of Southern California
Center for Software Engineering
System Boundary and Environment
Users
Administrators
Web portal
Search/navigation engine
Feedback channels
eDBA functions
eBASE content
FAQ, Chat/Discussion, Email,
etc.
...
Interoperators
Middleware for portal, DB, archive
management
August 2001
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USC
C S E
University of Southern California
Center for Software Engineering
eBASE Content
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Empirical Data and Models: Initial focus on defect reduction
– Organized around opportunity tree
– Including metadata, references, crosslinks, implications for project
practice; comments and contributions area
– Basic search and navigation capabilities
– Ability to enter user-project metadata, do similarity-search for most
relevant results
Project Archives: Initially MBASE, others TBD
– Organized around project guidelines (MBASE, Q-Labs, BORE, …)
– Including source artifacts and project data (effort, schedule, defects,
customer satisfaction, benefits realized)
Empirical Research Methods
– E.g. experimenting, data mining, model building
– Usage guidelines and experience reports, references, cross links;
comments and contributions area
– Common experiments
eBASE access and usage data
…
August 2001
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USC
C S E
University of Southern California
Center for Software Engineering
Major Project Constraints
• Prototype eBASE by Aug. 20, 2001
• Initial Operational Capability by May 5, 2002
– USC student team project
• Incremental approach; opportunistic priorities
– COTS, defect reduction data
– Available tools: portal, search, FAQ, chat, …
• Dependability and Access Control
• Low cost of usage
August 2001
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USC
C S E
University of Southern California
Center for Software Engineering
Proposed Metadata Categories
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•
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•
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Type of application (MIS, e-commerce, transportation,
manufacturing, telecom, computer products, engineering &
scientific, aerospace, public services, …)
Application profile (see attached method pattern categories
Developer skills and experience (students: undergraduate, grad,
professional, well below average … well above average)
Platform (mainframe, client, server, network, applications generator,
…)
Process Drivers (ranked): Requirements, cost, schedule,
dependability, option exploration
Size of project (maximum team size)
Time Period (beginning, end dates; associated milestones)
Experimental/Observational
Assessment of Data Quality (weakly defined and reported … very
thoroughly defined and reported)
August 2001
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