Measuring Software Reliability: A Trend Using Machine Learning Techniques

Kumar, Nishikant; Banerjee, Soumya

doi:10.1007/978-3-319-12883-2_28

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…One of the important advantages of the models is that they usually do not use any prior assumptions and are based only on fault history data. Many non-parametric SRGMs have been proposed in the literature [2,3,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. Contributions to this field are continuing, For example, Barghout [22] presented a new non-parametric model which is based on the idea of separation of concern between the long term trend in reliability growth and the local behaviour.…”

Section: Introductionmentioning

confidence: 99%

A robust weighted SVR-based software reliability growth model

Utkin

Coolen

2018

Reliability Engineering & System Safety

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Section: Introductionmentioning

confidence: 99%

A robust weighted SVR-based software reliability growth model

Utkin

Coolen

2018

Reliability Engineering & System Safety

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“…Comparing to conventional models, such white-noise-based approach assumes the perfect debugging and no doubt provides closer approximation to uncertain fluctuations in reality but with great mathematical simplicity. Debugging activity is usually imperfect in piratical software development and recent data [28,29] show that the fault detection is highly susceptible to noise and is generally correlated; thus earlier assumptions become problematic. Thus, because of its mathematical simplicity, it may also considerably underestimate the imperfect debugging process and the temporal correlation in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Software Reliability Growth Model with Partial Differential Equation for Various Debugging Processes

Yao²

2016

Mathematical Problems in Engineering

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Most Software Reliability Growth Models (SRGMs) based on the Nonhomogeneous Poisson Process (NHPP) generally assume perfect or imperfect debugging. However, environmental factors introduce great uncertainty for SRGMs in the development and testing phase. We propose a novel NHPP model based on partial differential equation (PDE), to quantify the uncertainties associated with perfect or imperfect debugging process. We represent the environmental uncertainties collectively as a noise of arbitrary correlation. Under the new stochastic framework, one could compute the full statistical information of the debugging process, for example, its probabilistic density function (PDF). Through a number of comparisons with historical data and existing methods, such as the classic NHPP model, the proposed model exhibits a closer fitting to observation. In addition to conventional focus on the mean value of fault detection, the newly derived full statistical information could further help software developers make decisions on system maintenance and risk assessment.

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A new boosting-based software reliability growth model

Utkin

Coolen

2020

Communications in Statistics - Theory and Methods

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A new software reliability growth model (SRGM) called RBoostSRGM is proposed in this paper. It can be regarded as a modification of the boosting SRGMs through the use of a reduced set of weights to take into account the behaviour of the software reliability during the debugging process and to avoid overfitting. The main idea underlying the proposed model is to take into account that training data at the end of the debugging process may be more important than data from the beginning of the process. This is modelled by taking a set of weights which are assigned to the elements of training data, i.e., to the series of times to software failures. The second important idea is that this large set is restricted by the imprecise ε-contaminated model. The obtained RBoostSRGM is a parametric model because it is tuned in accordance with the contamination parameter ε. As a variation to this model, we also consider the use of the Kolmogorov-Smirnov bounds for the restriction of the set of weights. Various numerical experiments with data sets from the literature illustrate the proposed model and compare it with the standard non-parametric SRGM and the standard boosting SRGM.

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Measuring Software Reliability: A Trend Using Machine Learning Techniques

Cited by 3 publications

References 29 publications

A robust weighted SVR-based software reliability growth model

A robust weighted SVR-based software reliability growth model

Software Reliability Growth Model with Partial Differential Equation for Various Debugging Processes

A new boosting-based software reliability growth model

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