Quantitative reliability and availability assessment for critical systems including software

Hecht, Myron; Tang, Dong; Hecht, H.; Brill, R.W.

doi:10.1109/cmpass.1997.613307

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…The failure of cmpt A to a 5 The SPNs were input to the Stochastic Petri Net Package (SPNP v. 6) tool in CSPL (C-based Stochastic Petri net Language). 6 For Markov analysis, the time to failure of all components is assumed to be exponentially distributed. if (mark("controllerLOS") > 0) return controller_rate * 10000; if ((mark("controllerDegraded") > 0) || (mark("tubingDegraded") > 0)) return controller_rate * 100; return controller_rate;} lost stability mode causes the failure rate of a cmpt B to increase by 10 4 (7) .…”

Section: Coincident Failures (Cfs)mentioning

confidence: 99%

“…Severity is an important candidate to weigh the data used in reliability calculations a n d m u s t b e incorporated into the model to determine the probability that the system survives, including efficient or acceptable degraded operation 1 . Failure severity has been studied in the context of gracefully degrading systems -using Markov models to model a multiprocessor system in [5] and a set of radars in an air traffic control system in [6].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the effect of failure severity, coincident failures and usage-profiles on the reliability of embedded control systems

Sheldon

Jerath

2004

Proceedings of the 2004 ACM Symposium on Applied Computing

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Very faithful but intractable Faithful but relatively intractable Relatively faithful and tractable Unfaithful but very tractable System Under Study (Proposed / Exisitng) REAL SYSTEMChallenges: (1) large state space, (2) model stiffness (relative order of magnitude among parameters) and, (3) the memoryless property assumes that events are independent and identically distributed.A model is always a compromise between faithfulness and simplicity. Results should be precise and correct (model predictions should be realistic and accurate based on a well founded mathematical theory, such as reliability theory). Model 1 Model 2 Model 3 Model 4 Fig. 1: Precise realistic models. ABSTRACTThe increasingly ubiquitous use of embedded systems to manage and control our technologically (ever-increasing) complex lives makes us more vulnerable than ever before. Knowing how reliable such systems are is absolutely necessary especially for safety, mission and infrastructure critical applications. This paper presents a structured compositional modeling method for assessing reliability based on characteristic data and stochastic models. We illustrate this using a classic embedded control system (sensor-inputs | processing | actuator-outputs), Anti-lock Braking System (ABS) and empirical data. Special emphasis is laid on modeling extra-functional characteristics of severity of failures, coincident failures and usage-profiles with the goal of developing a modeling strategy that is realistic, generic and extensible. The validation approach compares the results from the two separate models. The results are comparable and indicate the effect of coincident failures, failure severity and usage-profiles is predictable.

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Section: Coincident Failures (Cfs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the effect of failure severity, coincident failures and usage-profiles on the reliability of embedded control systems

Sheldon

Jerath

2004

Proceedings of the 2004 ACM Symposium on Applied Computing

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“…Severity is an important candidate to weight the data used in reliability calculations and must be incorporated into the model to determine the probability that the system survives, including efficient or acceptable levels of degraded operation. Severity of failures has been considered in the context of gracefully degrading systems in [11] and modelled using Markov Reward Models in [17].…”

Section: Predicting Reliability Using Stochastic Formalismsmentioning

confidence: 99%

Assessment of High Integrity Software Components for Completeness, Consistency, Fault-Tolerance, and Reliability

Kim

Jerath

Sheldon

2003

Component-Based Software Quality

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Abstract. The use of formal model based (FMB) methods to evaluate the quality of components is an important research area. Except for a growing number of exceptions, FMB methods are still not really used in practice. This chapter presents two case studies that illustrate the value of FMB approaches for developing and evaluating componentbased software. In the first study, Zed (or Z) and Statecharts are used to evaluate (a priori) the software requirement specification of a Guidance Control System for completeness, consistency and fault-tolerance. The second study evaluates (post-priori) the reliability of a complex vehicle system using Stochastic Activity Networks (SANs). The FMB approach presented here provides further evidence that such methods can indeed be useful by showing how these two different industrial strength systems were assessed and the results. Clearly, future investigations of this nature will help to convince software system developers using component based approaches that such FMB methods should be considered as a valuable tool toward improving the software product lifecycle (quality, schedule and cost).

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“…Software developed by SoHaR. 4 MEADEP creates models hierarchically. A total of 11 submodels were created as shown in Fig.…”

Section: Dependability Modelmentioning

confidence: 99%

Use of Combined System Dependability and Software Reliability Growth Models

Hecht

2002

Int. J. Rel. Qual. Saf. Eng.

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This paper describes how MEADEP, a system level dependability prediction tool, and CASRE, a software reliability growth prediction tool can be used together to predict system reliability (probability of failure in a given time interval), availability (proportion of time service is available), and performability (reward-weighted availability). The system includes COTS hardware, COTS software, radar, and communication gateways. The performability metric also accounts for capacity changes as processors in a cluster fail and recover. The Littlewood Verall and Geometric model is used to predict reliability growth from software test data this prediction is integrated into a system level Markov model that incorporates hardware failures and recoveries, redundancy, coverage failures, and capacity. The results of the combined model can be used to predict the contribution of additional testing upon availability and a variety of other figures of merit that support management decisions.

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Quantitative reliability and availability assessment for critical systems including software

Cited by 18 publications

References 12 publications

Assessing the effect of failure severity, coincident failures and usage-profiles on the reliability of embedded control systems

Assessing the effect of failure severity, coincident failures and usage-profiles on the reliability of embedded control systems

Assessment of High Integrity Software Components for Completeness, Consistency, Fault-Tolerance, and Reliability

Use of Combined System Dependability and Software Reliability Growth Models

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