Validation of a software dependability tool via fault injection experiments

Benso, Alfredo; Carlo, Stefano Di; Natale, Giorgio Di; Tagliaferri, Luca; Prinetto, P.

doi:10.1109/olt.2001.937809

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…SIHFT detects or tolerates faults in the hardware using software methods and without referring to any special hardware for error detection or fault tolerance. Several SIHFT techniques have been proposed in order to deal with different types of hardware errors such as Algorithm Based Fault Tolerance (ABFT) [5], Assertions, and Variable Duplication [6].…”

Section: B Software Fault Tolerancementioning

confidence: 99%

A survey on simulation-based fault injection tools for complex systems

Kooli

Natale

2014

2014 9th IEEE International Conference on Design &Amp; Technology of Integrated Systems in Nanoscale Era (DTIS)

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International audienceDependability is a key decision factor in today's global business environment. A powerful method that permits to evaluate the dependability of a system is the fault injection. The principle of this approach is to insert faults into the system and to monitor its responses in order to observe its behavior in the presence of faults. Several fault injection techniques and tools have been developed and experimentally tested. They could be mainly grouped into three categories: hardware fault injection, simulation-based fault injection, and emulation-based fault injection. This paper presents a survey on the simulation-based fault injection techniques, with a focus on complex micro-processor based systems

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Section: B Software Fault Tolerancementioning

confidence: 99%

A survey on simulation-based fault injection tools for complex systems

Kooli

Natale

2014

2014 9th IEEE International Conference on Design &Amp; Technology of Integrated Systems in Nanoscale Era (DTIS)

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“…The overall idea is that each output already includes the contribution of the corresponding operands that can therefore be excluded from the set of resources to consider (Algorithm 1, row [14][15][16]. If more than one output is computed based on the same set of operands, only one of these outputs must be considered (Algorithm 1, row [18][19][20][21][22][23][24][25]. Whenever a storage element is written, the operands used during the last instruction targeting the same resource must be considered again (Algorithm 1, row [14][15][16].…”

Section: Analytical Modelmentioning

confidence: 99%

“…To efficiently tradeoff between fault tolerance cost and system reliability one has to ask: what is the probability of a program P to have a correct execution state given a certain hardware (raw) soft-error rate? Fault injection is a viable solution to answer this question [18], [19], [20]. However, it can be very expensive and time consuming.…”

Section: Introductionmentioning

confidence: 99%

Statistical Reliability Estimation of Microprocessor-Based Systems

Savino

Carlo

Politano

et al. 2012

IEEE Trans. Comput.

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What is the probability that the execution state of a given microprocessor running a given application is correct, in a certain working environment with a given soft-error rate? Trying to answer this question using fault injection can be very expensive and time consuming. This paper proposes the baseline for a new methodology, based on microprocessor error probability profiling, that aims at estimating fault injection results without the need of a typical fault injection setup. The proposed methodology is based on two main ideas: a one-time fault-injection analysis of the microprocessor architecture to characterize the probability of successful execution of each of its instructions in presence of a soft-error, and a static and very fast analysis of the control and data flow of the target software application to compute its probability of success. The presented work goes beyond the dependability evaluation problem; it also has the potential to become the backbone for new tools able to help engineers to choose the best hardware and software architecture to structurally maximize the probability of a correct execution of the target software.

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“…by a set of carefully chosen software error detection mechanisms [7]. These techniques include Algorithm Based Fault Tolerance (ABFT) [8], Assertions, and Variable Duplication [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Data criticality estimation in software applications

Benso

Carlo

Natale

et al.

International Test Conference, 2003. Proceedings. ITC 2003.

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In safety-critical applications it is often possible to exploit software techniques to increase system's faulttolerance. Common approaches are based on data redundancy to prevent data corruption during the software execution. Duplicating most critical variables only can significantly reduce the memo ry and performance overheads, while still guaranteeing very good results in terms of fault-tolerance improvement. This paper presents a new methodology to compute the criticality of variables in target software applications. Instead of resorting to time consuming fault injection experiments, the proposed solution is based on the runtime analysis of the variables' behavior logged during the execution of the target application under different workloads.

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Validation of a software dependability tool via fault injection experiments

Cited by 8 publications

References 17 publications

A survey on simulation-based fault injection tools for complex systems

A survey on simulation-based fault injection tools for complex systems

Statistical Reliability Estimation of Microprocessor-Based Systems

Data criticality estimation in software applications

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