Performance Analysis of RGMs by using Supervised Learning Techniques
[Full Text]
AUTHOR(S)
Y Vamsidhar, P Samba Siva Raju, T Ravi Kumar
KEYWORDS
Software Reliability, Software Reliability Growth Models (SRGMs), AdaBoosting Algorithm, Least Square Estimation, Maximum Likelihood Estimation.
ABSTRACT
Software reliability is one of a number of aspects of computer software which can be taken into consideration when
determining the quality of the software. Building good reliability models is one of the key problems in the field of software reliability.
A good software reliability model should give good predictions of future failure behavior, compute useful quantities and be widely
applicable. Software Reliability Growth Models (SRGMs) are very important for estimating and predicting software reliability. An ideal
SRGM should provide consistently accurate reliability estimation and prediction across different projects. However, that there is no single
such model which can obtain accurate results for different cases. The reason is that the performance of SRGMs highly depends on the
assumptions on the failure behavior and the application data-sets. In other words, many models may be shown to perform well with one
failure data-set, but bad with the other data-set.
Thus, combining some individual SRGMs than single model is helpful to obtain more accurate estimation and prediction. SRGM
parameters are estimated using the least square estimation (LSE) or Maximum Likelihood Estimation (MLE). Several combinational
methods of SRGMs have been proposed to improve the reliability estimation and prediction accuracy. The AdaBoosting algorithm
is one of the most popular machine learning algorithms. An AdaBoosting based Combinational Model (ACM) is used to combine the
several models. The key idea of this approach is that we select several SRGMs as the weak predictors and use AdaBoosting algorithm
to determine the weights of these models for obtaining the final linear combinational model. In this paper, the Fitness and Prediction of
various Software Reliability Growth Models (SRGMs) can be compared with AdaBoosting based Combinational Model (ACM) with the
help of Maximum likelihood estimation to estimate the model parameters.
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