An experimental comparison of fault and error injection

Christmansson, J.; Hiller, Martin; Rimen, M.

doi:10.1109/issre.1998.730903

Cited by 26 publications

(16 citation statements)

References 9 publications

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“…An error is said to be detected if it is detected at least once during the arrestment. These experiments were performed on a real setup of the arrestment system (real hardware, real software, simulated environment-not a simulation run on a desktop computer) using the FIC 3 -tool (see [3] for details). The results are summarized in Table 6.…”

Section: Comparison 2: Errors In Random Locations In Memorymentioning

confidence: 99%

EPIC: profiling the propagation and effect of data errors in software

Hiller

Jhumka

Suri

2004

IEEE Trans. Comput.

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Abstract-We present an approach for analyzing the propagation and effect of data errors in modular software enabling the profiling of the vulnerabilities of software to find 1) the modules and signals most likely exposed to propagating errors and 2) the modules and signals which, when subjected to error, tend to cause more damage than others from a systems operation point-of-view. We discuss how to use the obtained profiles to identify where dependability structures and mechanisms will likely be the most effective, i.e., how to perform a cost-benefit analysis for dependability. A fault-injection-based method for estimation of the various measures is described and the software of a real embedded control system is profiled to show the type of results obtainable by the analysis framework.

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Section: Comparison 2: Errors In Random Locations In Memorymentioning

confidence: 99%

EPIC: profiling the propagation and effect of data errors in software

Hiller

Jhumka

Suri

2004

IEEE Trans. Comput.

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“…But only experimental evidence can indicate the area of validity of these "theoretically wrong" methods. Research comparing empirically the measures produced by different fault injection methods [18][19][20] is also an indirect contribution in this direction.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Assessing Asymmetric Fault-Tolerant Software

Popov

Strigini

2010

2010 IEEE 21st International Symposium on Software Reliability Engineering

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract -The most popular forms of fault tolerance against design faults use "asymmetric" architectures in which a "primary" part performs the computation and a "secondary" part is in charge of detecting errors and performing some kind of error processing and recovery. In contrast, the most studied forms of software fault tolerance are "symmetric" ones, e.g. Nversion programming. The latter are often controversial, the former are not. We discuss how to assess the dependability gains achieved by these methods. Substantial difficulties have been shown to exist for symmetric schemes, but we show that the same difficulties affect asymmetric schemes. Indeed, the latter present somewhat subtler problems. In both cases, to predict the dependability of the fault-tolerant system it is not enough to know the dependability of the individual components. We extend to asymmetric architectures the style of probabilistic modeling that has been useful for describing the dependability of "symmetric" architectures, to highlight factors that complicate the assessment. In the light of these models, we finally discuss fault injection approaches to estimating coverage factors. We highlight the limits of what can be predicted and some useful research directions towards clarifying and extending the range of situations in which estimates of coverage of fault tolerance mechanisms can be trusted. Permanent repository link

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“…Each defect can be classified to belong to one of these types. These types have later been used to build libraries for injection of artificial faults in systems, for instance [Christmansson and Chillarege, 1996;Christmansson et al, 1998;Durães and Madeira, 2006].…”

Section: Software Relatedmentioning

confidence: 99%

Robustness Evaluation of Operating Systems

Johansson

2008

Information Assurance

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SummaryThe premise behind this thesis is the observation that Operating Systems (OS), being the foundation behind operations of computing systems, are complex entities and also subject to failures. Consequently, when they do fail, the impact is the loss of system service and the applications running thereon. While a multitude of sources for OS failures exist, device drivers are often identified as a prominent cause behind failures.In order to characterize the impact of driver failures, at both the OS and application levels, this thesis develops a framework for error propagation-based robustness profiling for an OS. The framework is first developed conceptually and then experimentally validated on a real OS, namely Windows CE .Net. The choice of Windows CE is driven by its representativeness for a multitude of OS's, as well as the ability to customize the OS components for particular needs.For experimental validation, fault injection is a prominent technique that can be used to simulate faults (or errors) in the system by inserting synthetic ones and study their effect. Three key questions with such a technique are where, what and when to inject faults. This thesis shows how injecting errors at the interface between drivers and the OS can be very effective in evaluating the effects driver faults can have.To quantify the OS's robustness, this thesis defines a series of error propagation measures, specifically tailored for device drivers. These measures allow for quantifying and comparing both individual services and device drivers on their susceptibility and diffusing abilities.This thesis compares three contemporary error models on their suitability for robustness evaluation. The classical bit-flip model is found to identify a higher number of severe failures in the system. It also identifies failures for more services than both other models, data type and fuzzing. However, its main drawback is that it requires substantially more injections than the other two. Fuzzing, even though not giving rise to as many failures is able to find new additional services with severe failures.A careful study of the injections performed with the bit-flip model shows that only a few bits are generally useful for identifying new services with robustness weaknesses. Consequently, a new composite model is proposed, combining the most effective bits of the bit-flip model with the fuzzing model's ability to identify new services, giving rise to new model without loss of important information and at the same time incurring a moderate number of injections.To answer the question of when to inject an error this thesis proposes a novel model of a driver's usage profile, focusing on high-level operations being carried out. It guides the injection of errors to instances when the driver is carrying out specific operations. Results from extensive fault injection experiments show that more service vulnerabilities can be discovered. Furthermore, a priori profiling of the drivers can show how effective the proposed approach will be. vi

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An experimental comparison of fault and error injection

Cited by 26 publications

References 9 publications

EPIC: profiling the propagation and effect of data errors in software

EPIC: profiling the propagation and effect of data errors in software

Assessing Asymmetric Fault-Tolerant Software

Robustness Evaluation of Operating Systems

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