Transcript Software Reliability Through Hardware Reliability NASA OSMA SAS '01 Dolores R. Wallace

NASA OSMA SAS '01
Software Reliability Through
Hardware Reliability
Dolores R. Wallace
SRS Information Services
Software Assurance Technology Center
http://satc.gsfc.nasa.gov/
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The Problem
• Critical NASA systems must execute successfully for a
specified time under specified conditions -- Reliability
• Most systems rely on software
• Hence, a means to measure software reliability is essential to
determining readiness for operation
• Software reliability modeling provides one data point for
reliability measurement
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The Issues
• Identify mathematics of hardware reliability not used in
software
• Identify differences between hardware, software affecting
reliability measurement
• Develop improvements to software reliability modeling – Dr.
Norman Schneidewind, Naval Postgraduate School
• Develop and implement scenario for typical application on
GSFC project data
• Identify how software reliability modeling can be used at
GSFC
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The Mathematics
Typical Statistical
Distributions
•Exponential
•Gamma
•Weibull
•Binomial
•Poisson
•Normal
•Lognormal
•Bayes
•Markov
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Sample software
reliability models
•Musa Basic:
m(t) = ß0(1-exp(-ß1t))
•Schneidewind:
D(T) = (α/β) [1-exp(-β ((Ts+1)))] + Xs-1
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Differences between Hardware
and Software
Hardware
Software
• Deterioration over time
• No deterioration over time
•Faults removed after build
• Faults possibly entered
during fault correction
•Initial failures during test
period, early use; then
stable
•Models do not account for
time to correct faults
•Software test may not be
continuous time
•Design faults removed before
manufacture
•No faults entered during life
•Initial use, end of life failures
•No need to allow time to
correct faults
• Models based on continuous
use
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Fault Correction Adjustments
• Reliability growth occurs from fault correction
• Failure correction proportional to rate of failure detection
• Adjusted model with delay dT (based on queuing service)
but same general form as faults detected at time T
• Process: use Schneidewind model to get parameters; apply to
revised model via spreadsheet
• Results
– Show reliability growth due to fault correction
– Predict stopping rules for testing
• Optimal Selection of Failure Data
• Next: GSFC data; Fault insertion
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Applying Software Reliability
Modeling
• The modeling process
– Apply AIAA Recommended Practice
– Learn about the system
• Data collection requirements
– Dates of failure, fix
– Activities/ phase when failures occur
– Preparation of the data (interval; time between failure)
• Available software tools
– Public domain SMERFS^3
– Loglet: commercial, but for our validation
• Interpretation of results
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Example of SMERFS^3 Ouput
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Loglet – same data
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Options for SRM at NASA
• SATC Service
– Projects submit failure data and project information
– SATC executes models and prepares analysis
• Deployment to Project staff
– SATC provides training and public domain tool
– Project staff utilize the models
• Partnering
– SATC provides tutorials and tools
– Project staff may prepare the data and SATC the analysis
– Project staff may exercise the models but discuss results
with SATC
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Proposed Next Steps
• Continue research on improvements to existing software
reliability models
• Explore non-parametric methods
• Complete experiments with NASA data
• Provide technology transfer of knowledge
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