The discrete time hyperexponential model for software reliability growth evaluation

Kaâniche, Mohamed; Kanoun, Karama

doi:10.1109/issre.1992.285857

Cited by 12 publications

(4 citation statements)

References 18 publications

(10 reference statements)

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“…Similarly, reliability behaviour of a bank transaction processing software system should be assessed in terms of how many transactions are successful. Obviously, for those systems, the time‐based reliability measurement is essentially discrete rather than continuous …”

Section: Software Reliability Modellingmentioning

confidence: 99%

An Integrated Framework for Developing Discrete‐Time Modelling in Software Reliability Engineering

Shatnawi

2016

Quality & Reliability Eng

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In the software reliability engineering literature, few attempts have been made to study the fault debugging environment using discrete-time modelling. Most endeavours assume that a detected fault to have been either immediately removed or is perfectly debugged. Such discrete-time models may be used for any debugging environment and may be termed blackbox, which are used without having prior knowledge about the nature of the fault being debugged. However, if one has to develop a white-box model, one needs to be cognizant of the debugging environment. During debugging, there are numerous factors that affect the debugging process. These factors may include the internal, for example, fault density, and fault debugging complexity and the external that originates in the debugging environment itself, such as the skills of the debugging team and the debugging effort expenditures. Hence, the debugging environment fault removal may take a longer time after having been detected. Therefore, it is imperative to clearly understand the testing and debugging environment and, hence, the urgency to develop a model. The model ought to take into account the fault debugging complexity and incorporate the learning phenomenon of the debugger under imperfect debugging environment. This objective dictates developing a framework through an integrated modelling approach based on nonhomogenous Poisson process that incorporates these realistic factors during the fault debugging process. Actual software reliability data have been used to demonstrate applicability of the proposed integrated framework.

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Section: Software Reliability Modellingmentioning

confidence: 99%

An Integrated Framework for Developing Discrete‐Time Modelling in Software Reliability Engineering

Shatnawi

2016

Quality & Reliability Eng

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“…A representative sample is (in discrete time) [2], [7], [9] and (in continuous time) [6], [3], [4], [5], [10].…”

Section: Software Dependability With a Markovian Structural Modelmentioning

confidence: 99%

Availability modeling of modular software

Ledoux

1999

IEEE Trans. Rel.

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Summary & Conclusions -Dependability evaluation is a basic component in the assessment of the quality of repairable systems. We develop here a general model specifically designed for software systems that allows the evaluation of different dependability metrics, in particular, of availability measures. The model is of the structural type, based on Markov process theory. In particular, it can be viewed as a attempt to overcome some limitations of the well-known Littlewood's reliability model for modular software. We give both the mathematical results necessary to the transient analysis of this general model and the algorithms that allow to evaluate it efficiently. More specifically, from the parameters describing : the evolution of the execution process when there is no failure, the failure processes together with the way they affect the execution, and the recovery process, we obtain the distribution function of the number of failures on a fixed mission period. In fact, we obtain dependability metrics which are much more informative than the usual ones given in a white-box approach.We briefly discuss the estimation procedures of the parameters of the model. From simple examples, we illustrate the interest in such a structural view and we explain how to take into account reliability growth of part of the software with the transformation approach developed by Laprie and al.Finally, the complete transient analysis of our model allows to discuss in our context the Poissonian approximation reported by Littlewood for its model.

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“…Bai 16 has developed an extended Markov Bayesian network (ENBM) model for the reliability evaluation with an operational profile and compared its performance with the Kaaniche-Kanoun model (K&K model) 17 on SPACE failure data. However, it does not consider the experts' opinion in the early phases of the SDLC.…”

Section: Introductionmentioning

confidence: 99%

Software reliability analysis for safety‐critical and control systems

Kumar

Singh

Kumar

2019

Quality & Reliability Eng

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The transition from analog to digital safety-critical instrumentation and control (I&C) systems has introduced new challenges for software experts to deliver increased software reliability. Since the 1970s, researchers are continuing to propose software reliability models for reliability estimation of software. However, these approaches rely on the failure history for the assessment of reliability. Due to insufficient failure data, these models fail to predict the reliability of safety critical systems. This paper utilizes the Bayesian update methodology and proposes a framework for the reliability assessment of the safety-critical systems (SCSs). The proposed methodology is validated using experiments performed on real data of 12 safety-critical control systems of nuclear power plants.We believe that SRM for SCS must have some special desirable characteristics:1. There should be acceptable documentation of the method and its applications that can be understood and evaluated. 2. There should be a strong justification of the assumptions made. 3. It should be possible to consider the specific operating conditions of the software. 4. It should be able to address all the uncertainties for better accuracy. 5. There should be sufficiency of model parameters. 6. The models should have proper verification and validation ways.Several models do not have description of intended uses of the method and all assumptions. 6,7 In general, the existing SRM assumes unrealistic assumptions and use failure history to estimate the parameters of the models in order to calculate the reliability. As of our knowledge, we have found very few SRM which consider the specific operating conditions of the software. 8 Software reliability growth model (SRGM) estimates the software reliability with high accuracy, if there is sufficiency of failure data and therefore are not suitable to SCS.Software errors are generally due to unstated, ambiguous, and unrealistic requirements or because of design errors. From this, we believe that if it is possible to formulate any model that can accommodate errors in each phase of software development life cycle (SDLC) and also can propagate them in next phase, prior to the completion of software development, it will give more accurate reliability prediction results. A perfect designed software requirement and specification (SRS) not only depends on the requirements and analysis but also on the expert's opinion, the domain knowledge, and the experience of the SRS designing team, the resources available, and many other constraints. Taking these as the inputs for the requirements phase, a perfect SRS is designed. These inputs may be considered as "safety cases" or "safety arguments" as these are the collection of evidence responsible for the proper development of final product. Evidence for safety cases or safety arguments for each phase exists. Using these evidences, we can create BBN model for each phase of life cycle model and calculate the inferred probability using the safety argument values. A proper conditio...

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The discrete time hyperexponential model for software reliability growth evaluation

Cited by 12 publications

References 18 publications

An Integrated Framework for Developing Discrete‐Time Modelling in Software Reliability Engineering

An Integrated Framework for Developing Discrete‐Time Modelling in Software Reliability Engineering

Availability modeling of modular software

Software reliability analysis for safety‐critical and control systems

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