Software Reliability Growth Modeling: Comparison Between Non-Linear-Regression Estimation and Maximum-Likelihood-Estimator Procedures

Paper #:
  • 2018-01-0006

Published:
  • 2018-04-03
Abstract:
Automotive software complexity has been growing rapidly with time. The demand for automation in automotive segment including autonomous automobiles and software based products has caught the attention of researchers. Hence, it is necessary to check the complexity of automotive software and their reliability growth. Testing in the field of software artefact is resource intensive exercise. If project managers are able to put forward testing activities well then the testing resource consumptions may be much more resource/cost efficient. Reliability is checked under the software testing phase of software engineering. Software reliability growth models(SRGMs) are used to determine the reliability change. Reliability is analysed using different models which may be based on Non-Homogeneous Poisson Process (NHPP), Markov process or Bayesian models. Reliability check is also done after testing phase to estimate the latent faults prediction to assess the maturity of automotive software.For parameter estimation two techniques have been used namely maximum likelihood and method of least squares. The two techniques under comparative estimation include Maximum-Likelihood-Estimator(MLE) and Non-Linear-Regression(NLR) estimators. Assessment of prediction accuracy using relative error metric i.e. Balanced-Prediction-Relative-Error(BPRE) lower than 5% is reported . Further in the paper we compare between these two estimation procedures for their usability and applicability in correlation with SRGMs. The data used for this study is time-domain failure. In the data software faults have been reported with their time between failures (TBF). Results obtained highlight the fact that NLR is a reasonable estimator for fitting the data to observed failure data, while MLE is a better estimator for making reliable predictions.
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