Software-Reliability Modeling: The Case for Deterministic Behavior

Dick, Scott; Bethel, Cindy L.; Kandel, A.

doi:10.1109/tsmca.2006.886364

Cited by 21 publications

(8 citation statements)

References 29 publications

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“…Fenton and Neil [] claim that software defect data is autocorrelated, meaning that defects are related over time. Although they do not provide any (statistical) evidence of their claim, software failures are the result of human mistakes, which indeed do not appear to be random in nature [Dick et al, ]. One defect can induce the next fault.…”

Section: Estimating Time‐to‐marketmentioning

confidence: 99%

“…Others have taken different approaches and have used change data [Mockus et al, 2003], expert estimations [Bai et al, 2003], and project plannings [McConnell, 1996;Staron and Meding, 2008] to predict the trend with which defects will be discovered. Furthermore, various researchers have proposed to model this trend as time series [Dick et al, 2007;Herraiz et al, 2007;Kenmei et al, 2008;Raja et al, 2009]. In this article, we have shown that there is no generally applicable model that works in all circumstances.…”

Section: Defect Trendsmentioning

confidence: 99%

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Balancing Time‐to‐Market and Quality in Embedded Systems

Spek

Verhoef

2013

Systems Engineering

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Finding a balance between the time-to-market and quality of a delivered product is a daunting task. The optimal release moment is not easily found. We propose to use historical project data to monitor the progress of running projects. From the data we inferred a formula providing a rough indication of the number of defects given the effort spent thus far (effort-to-defect formula). Furthermore, we provide a worst case bound to the allowed number of residual defects at the end of a project in order to achieve the required level of quality. For this purpose we slightly modified a well-known reliability growth model by Bishop and Bloomfield. It turned out that the software in Philips' MRI scanners has a defect rate of 1 per 1175 device-years of observation. This coincides with the second highest safety integrity level (SIL3) as defined in the IEC 61508 standard. The highest level (SIL4) is only attainable by applying redundancy. Finally, we combine the effort-to-defect formula with the reliability growth model to monitor the progress of a project and to determine when the required level of quality will be reached. We show that a common fault distribution, the Rayleigh model, is not necessarily the best model for predicting the number of residual defects in the system. Using a well-known data analysis approach called exploratory data analysis (EDA) we obtained an alternative model based on the Normal curve. We have evaluated the Rayleigh model and our model based on the Normal curve at Philips Healthcare MRI. The Normal curve predicts defects over time better than the Rayleigh model in the case of Philips Healthcare MRI. Furthermore, time series models (ARIMA) are also useful for accurately describing the defect trend, but are not suitable for long-term predictions. Finally, cost estimation models (COCOMO) lack the predictive capabilities of models fitted on the data using EDA. Their capabilities are limited compared to a model derived from data which reflects the constitutional knowledge of the actual realization of systems within a specific company. However, they can still be used to advantage when there is limited or no data available to use as a basis for lifting from gut feel to order of magnitude.

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Section: Estimating Time‐to‐marketmentioning

confidence: 99%

Section: Defect Trendsmentioning

confidence: 99%

Balancing Time‐to‐Market and Quality in Embedded Systems

Spek

Verhoef

2013

Systems Engineering

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“…The trend of time series data of software defects among successive testing phases is analyzed and the relationship between the trends of the time series data S. K. CHATURVEDI*, R. B. MISRA** Reliability Engineering Centre Indian Institute ofTechnology Kharagpur Kharagpur, West Bengal, PIN-721302, India * skcrec@hijli.iitkgp.ernet.in ** ravi@ee.iitkgp.ernet.in and software quality was investigated [9]. Dick et al [10] studied two sets of real-world software reliability data using the techniques of chaotic time-series analysis, and found that both appear to arise from a deterministic process, rather than a stochastic process, and that both show some evidence of chaotic dynamics. Due to the general non-linear mapping capabilities, neural networks have been applied in time series forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting time-between-failures of software using fuzzy time series approach

Yadav

Chaturvedi

Misra

2012

2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)

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The problem with many of the software reliability models is that they cannot deal with the situations where the time-between-failures information during software testing phase is associated with uncertainty due to fuzzy nature of software testing and debugging. Therefore, in this paper, a procedure is proposed to forecast times-between-failures of software during its testing phase by employing fuzzy time series approach, where time-between-failures of software is represented by a fuzzy set having trapezoidal membership function. The forecasting capability of the proposed approach is examined using software failure data of two real software development projects.

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“…The idea of chaos theory application in the reliability modeling appears in [17]. Two real data bases about software failures are processed by the methods of chaos theory in the paper [18]. It was shown that the deterministic model of failures is more adequate to the experimental data than the traditional stochastic models, for example, the modified Poisson's law, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Reliability Modeling and Optimization Using Fuzzy Logic and Chaos Theory

Ротштейн

Катєльніков

Pustylnik

2012

International Journal of Quality, Statistics, and Reliability

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Fuzzy sets membership functions integrated with logistic map as the chaos generator were used to create reliability bifurcations diagrams of the system with redundancy of the components. This paper shows that increasing in the number of redundant components results in a postponement of the moment of the first bifurcation which is considered as most contributing to the loss of the reliability. The increasing of redundancy also provides the shrinkage of the oscillation orbit of the level of the system’s membership to reliable state. The paper includes the problem statement of redundancy optimization under conditions of chaotic behavior of influencing parameters and genetic algorithm of this problem solving. The paper shows the possibility of chaos-tolerant systems design with the required level of reliability.

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Software-Reliability Modeling: The Case for Deterministic Behavior

Cited by 21 publications

References 29 publications

Balancing Time‐to‐Market and Quality in Embedded Systems

Balancing Time‐to‐Market and Quality in Embedded Systems

Forecasting time-between-failures of software using fuzzy time series approach

Reliability Modeling and Optimization Using Fuzzy Logic and Chaos Theory

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