HSFI: Accurate Fault Injection Scalable to Large Code Bases

Kouwe, Erik van der; Tanenbaum, Andrew S.

doi:10.1109/dsn.2016.22

Cited by 9 publications

(3 citation statements)

References 42 publications

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“…• Injecting faults inside a component (by mutating its source or binary code [16], [25]- [27]) can be inefficient, since component-internal injections are often dormant, i.e., the injections do not change the behavior of the component as perceived by the rest of the system; therefore, these dormant injections cannot test fault tolerance, and result in a waste of experimental time [12]- [14]. Instead, the injection of failure modes in component's interfaces and resources is more efficient, as it directly injects the intended effects of component-internal faults [17], [28]- [31].…”

Section: Fault Modeling In the Android Osmentioning

confidence: 99%

Dependability Assessment of the Android OS Through Fault Injection

et al. 2021

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The reliability of mobile devices is a challenge for vendors, since the mobile software stack has significantly grown in complexity. In this paper, we study how to assess the impact of faults on the quality of user experience in the Android mobile OS through fault injection. We first address the problem of identifying a realistic fault model for the Android OS, by providing to developers a set of lightweight and systematic guidelines for fault modeling. Then, we present an extensible fault injection tool (AndroFIT) to apply such fault model on actual, commercial Android devices. Finally, we present a large fault injection experimentation on three Android products from major vendors, and point out several reliability issues and opportunities for improving the Android OS. 2

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Section: Fault Modeling In the Android Osmentioning

confidence: 99%

Dependability Assessment of the Android OS Through Fault Injection

et al. 2021

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“…Based on this analysis, they defined a software fault model with 13 fault types, covering 60% of the sample of bugs in the open-source projects [14]. This fault model was used in several other tools, including SAFE [35], HSFI [36], and FastFI [37]. However, these tools focus on a fixed software fault model, with no ability to customize the injected faults according to the specific needs of a project or company.…”

Section: Related Workmentioning

confidence: 99%

ProFIPy: Programmable Software Fault Injection as-a-Service

Cotroneo,

De Simone,

Liguori

et al. 2020

Preprint

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In this paper, we present a new fault injection tool (ProFIPy) for Python software. The tool is designed to be programmable, in order to enable users to specify their software fault model, using a domain-specific language (DSL) for fault injection. Moreover, to achieve better usability, ProFIPy is provided as software-as-a-service and supports the user through the configuration of the faultload and workload, failure data analysis, and full automation of the experiments using containerbased virtualization and parallelization.

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“…Kao et al [19] invented "FINE", a fault injection and monitoring tool to inject both hardware-induced software errors and software faults. Kouwe and Tanenbaum [34] presented HSFI, a fault injection tool that injects faults with context information from source code and applies fault injection decisions efficiently on the binary.…”

Section: Related Workmentioning

confidence: 99%

TripleAgent: Monitoring, Perturbation and Failure-Obliviousness for Automated Resilience Improvement in Java Applications

Zhang

Monperrus

2019

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)

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In this paper, we present a novel resilience improvement system for Java applications. The unique feature of this system is to combine automated monitoring, automated perturbation injection, and automated resilience improvement. The latter is achieved thanks to the failure-oblivious computing, a concept introduced in 2004 by Rinard and colleagues. We design and implement the system as agents for the Java virtual machine. We evaluate the system on two real-world applications: a file transfer client and an email server. Our results show that it is possible to automatically improve the resilience of Java applications with respect to uncaught or mishandled exceptions.

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HSFI: Accurate Fault Injection Scalable to Large Code Bases

Cited by 9 publications

References 42 publications

Dependability Assessment of the Android OS Through Fault Injection

Dependability Assessment of the Android OS Through Fault Injection

ProFIPy: Programmable Software Fault Injection as-a-Service

TripleAgent: Monitoring, Perturbation and Failure-Obliviousness for Automated Resilience Improvement in Java Applications

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