Evaluation of standard reliability growth models in
the context of automotive software systems
SRGMs: Software Reliability
Growth Models
Rakesh Rana1, Miroslaw Staron1, Niklas Mellegård1, Christian Berger1,
Jörgen Hansson1, Martin Nilsson2, Fredrik Törner2
1Software Engineering division,
Department of Computer Science and Engineering,
Chalmers/ University of Gothenburg 2Volvo Cars Corporation
This Car Runs on Code
“It takes dozens of mircroprocessors running 100 million lines of
code to get a premium car out of the driveway, and this software is
only going to get more complex” -ieee spectrumRef: http://spectrum.ieee.org/green-tech/advanced-cars/this-car-runs-on-code
Reliability
*Reliability and dependability are very important features
of any computer system.
*Have we done enough testing?
*Is the software ready for release?
*How should we adjust/optimize our testing strategy?
SRGM -> Software Reliability and Maturity
SRGM -> Use for Automotive Software Projects
Data used (Automotive Project)
Mellegård, N., Staron, M., and Törner, F.: ‘A light-weight defect classification scheme for embedded
automotive software and its initial evaluation’
Different Software Reliability Growth Models
Model Name Model Type Mean Value Function Reference
Models with 2 parameters
Goel-Okumoto (GO) Concave 𝑚 𝑡 = 𝑎(1 − 𝑒−𝑏𝑡 ) [11]
Delayed S-shaped model S-shaped 𝑚 𝑡 = 𝑎(1 − (1 + 𝑏𝑡)𝑒−𝑏𝑡 ) [12]
Rayleigh model 𝑚 𝑡 = 𝑎𝑒−𝑏/𝑡
Models with 3 parameters
Inflection S-shaped model S-shaped 𝑚 𝑡 =
𝑎(1 − 𝑒−𝑏𝑡 )
(1 + 𝛽𝑒−𝑏𝑡 )
[9]
Yamada exponential imperfect
debugging model (Y-ExpI)
S-shaped 𝑚 𝑡 =
𝑎𝑏
∝ + 𝑏 (𝑒∝𝑡 − 𝑒−𝑏𝑡 )
[13]
Yamada linear imperfect
debugging model (Y-LinI)
S-shaped 𝑚 𝑡 = 𝑎 1 − 𝑒−𝑏𝑡 1 − ∝
𝑏 + ∝ 𝑎𝑡 [13]
Logistic population model S-shaped 𝑚 𝑡 = 𝑎
1 + 𝑒−𝑏 𝑡−𝑐 [14]
Gompertz model S-shaped 𝑚 𝑡 = 𝑎𝑒−𝑏𝑒−𝑐𝑡
[15]
Conclusions and further work
*Two parameters models: fit - reasonable, asymptotes -
unrealistic;
*Logistic and inflectionS: Best fit to our data among the
different models tried;
*Important factors: Using appropriate time scale.;
*Using parameter estimates from two parameter models
and current project information, can give useful insight for
optimizing the resource allocation going forward.
Summary and Impact
*Logistic and inflectionS and Gompertz model gives best
fit and asymptote predictions.
*Identifying right models and using SRGMs in the
company and automotive sector in general will:-
*Help assess the reliability of software developed and thus the
release readiness.
*Using SRGM during the project can help test and quality
managers to make optimal testing resource allocation decisions.
*Thus correct use of SRGMs help the company & the automotive
industry to develop and release high quality software.
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