On the emulation of software faults by software fault injection

Madeira, Henrique; Costa, D.; Vieira, Marco

doi:10.1109/icdsn.2000.857571

Cited by 92 publications

(44 citation statements)

References 17 publications

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“…First, the fault model for executable software models has a great impact on the validity of the re-sults, so a fine-tuned and validated fault model is necessary. We plan to use completed projects with code-level fault injection to statistically compare the effects of model-level and code-level faults, similarly to [17], where code-level fault injection was compared to real software faults. Secondly, a specific model checking algorithm could inherently optimize the structure of the state space without lazy evaluation injected into the model (see Section 4.4).…”

Section: Discussionmentioning

confidence: 99%

“…[13]). Studies also revealed that it is very hard to accurately simulate real-life faults with fault injection [17]. Constructing a realistic fault model is even harder in case only a design model is available.…”

Section: Software Fmeamentioning

confidence: 99%

“…For an extensive overview of mutation-based testing and fault models, the reader should refer to [16]. More studies about the various aspects of software fault injection and fault models can be found in [12,13,17,24].…”

Section: Software Fmeamentioning

confidence: 99%

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Model Checking-based Software-FMEA: Assessment of Fault Tolerance and Error Detection Mechanisms

Molnár

Majzik

2017

Period. Polytech. Elec. Eng. Comp. Sci.

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Failure Mode and Effects Analysis (FMEA) is a systematic IntroductionThe risk of failure is one of the main concerns of safetycritical systems. Certification requires the systematic analysis of potential failures, their causes and effects, and the evaluation of risk mitigation techniques used to reduce the chance and the severity of system-level failures.One of the first systematic techniques for failure analysis was Failure Mode and Effect Analysis (FMEA) [2]. FMEA is often the first step in reliability analysis, as it collects the potential failure modes of subsystems, their root causes and their effects on the whole system. Together with criticality analysis (often treated as part of FMEA, sometimes emphasized by the term FMECA), the output of FMEA serves as the basis of other qualitative and quantitative analyses, as well as design decisions regarding risk mitigation techniques.FMEA is usually applied at the hardware and communication level, where it requires a qualified analyst to collect postulated component failures and identify their effects on other components and the system level. In case of software (SW-FMEA), failure modes originate in different types of programming faults, commonly referred to as bugs. Due to the complex nature of software and the many types of potential bugs, it is much harder to collect failure modes and deduce their potential effects, so an automated mechanism is desired. This paper presents a way of automated SW-FMEA with the use of executable software models. Assuming a set of predefined fault types (programming faults) and a specification of safe behavior at the system level, the proposed approach applies model checking to systematically generate execution traces leading from fault activations to states that violate the specification of safe behavior (system-level failures). These traces can be used to understand and demonstrate fault propagation through the system and also as test sequences to reveal actual faults in the final product.Our approach improves existing model checking-based SW-FMEA by optimizing fault activations and using monitor components instead of a formal specification. The optimization helps in scaling the solution to apply it on more complex systems and situations, whereas using a monitor instead of formal

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Section: Discussionmentioning

confidence: 99%

Section: Software Fmeamentioning

confidence: 99%

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Model Checking-based Software-FMEA: Assessment of Fault Tolerance and Error Detection Mechanisms

Molnár

Majzik

2017

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Faults are injected as bit level faults (stuck-at, flips and masks). Xception was later used for other studies, including [Madeira et al, 2000.…”

Section: Fault Injectionmentioning

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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“…To some extent, SWIFI tools can be used to emulate software faults [10]. However, it has been found that some software faults can not be emulated by any SWIFI tool.…”

Section: Related Workmentioning

confidence: 99%