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Software fault-freeness and reliability predictions
Many software development practices aim at ensuring that software is correct, or fault-free. In safety critical applications, requirements are in terms of probabilities of certain behaviours, e.g. as associated to the Safety Integrity Levels of IEC 61508. The two forms of reasoning - about evidence of correctness and about probabilities of certain failures -are rarely brought together explicitly. The desirability of using claims of correctness has been argued by many authors, but not been taken up in practice. We address how to combine evidence concerning probability of failure together with evidence pertaining to likelihood of fault-freeness, in a Bayesian framework. We present novel results to make this approach practical, by guaranteeing reliability predictions that are conservative (err on the side of pessimism), despite the difficulty of stating prior probability distributions for reliability parameters. This approach seems suitable for practical application to assessment of certain classes of safety critical systems
Prediction intervals for reliability growth models with small sample sizes
Engineers and practitioners contribute to society through their ability to apply basic scientific principles to real problems in an effective and efficient manner. They must collect data to test their products every day as part of the design and testing process and also after the product or process has been rolled out to monitor its effectiveness. Model building, data collection, data analysis and data interpretation form the core of sound engineering practice.After the data has been gathered the engineer must be able to sift them and interpret them correctly so that meaning can be exposed from a mass of undifferentiated numbers or facts. To do this he or she must be familiar with the fundamental concepts of correlation, uncertainty, variability and risk in the face of uncertainty. In today's global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. Many organisations have shown that the first step to continuous improvement is to integrate the widespread use of statistics and basic data analysis into the manufacturing development process as well as into the day-to-day business decisions taken in regard to engineering processes.The Springer Handbook of Engineering Statistics gathers together the full range of statistical techniques required by engineers from all fields to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved
Checkpointing algorithms and fault prediction
This paper deals with the impact of fault prediction techniques on
checkpointing strategies. We extend the classical first-order analysis of Young
and Daly in the presence of a fault prediction system, characterized by its
recall and its precision. In this framework, we provide an optimal algorithm to
decide when to take predictions into account, and we derive the optimal value
of the checkpointing period. These results allow to analytically assess the key
parameters that impact the performance of fault predictors at very large scale.Comment: Supported in part by ANR Rescue. Published in Journal of Parallel and
Distributed Computing. arXiv admin note: text overlap with arXiv:1207.693
Amortising the Cost of Mutation Based Fault Localisation using Statistical Inference
Mutation analysis can effectively capture the dependency between source code
and test results. This has been exploited by Mutation Based Fault Localisation
(MBFL) techniques. However, MBFL techniques suffer from the need to expend the
high cost of mutation analysis after the observation of failures, which may
present a challenge for its practical adoption. We introduce SIMFL (Statistical
Inference for Mutation-based Fault Localisation), an MBFL technique that allows
users to perform the mutation analysis in advance against an earlier version of
the system. SIMFL uses mutants as artificial faults and aims to learn the
failure patterns among test cases against different locations of mutations.
Once a failure is observed, SIMFL requires either almost no or very small
additional cost for analysis, depending on the used inference model. An
empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL
can successfully localise up to 103 faults at the top, and 152 faults within
the top five, on par with state-of-the-art alternatives. The cost of mutation
analysis can be further reduced by mutation sampling: SIMFL retains over 80% of
its localisation accuracy at the top rank when using only 10% of generated
mutants, compared to results obtained without sampling
Cross-layer system reliability assessment framework for hardware faults
System reliability estimation during early design phases facilitates informed decisions for the integration of effective protection mechanisms against different classes of hardware faults. When not all system abstraction layers (technology, circuit, microarchitecture, software) are factored in such an estimation model, the delivered reliability reports must be excessively pessimistic and thus lead to unacceptably expensive, over-designed systems. We propose a scalable, cross-layer methodology and supporting suite of tools for accurate but fast estimations of computing systems reliability. The backbone of the methodology is a component-based Bayesian model, which effectively calculates system reliability based on the masking probabilities of individual hardware and software components considering their complex interactions. Our detailed experimental evaluation for different technologies, microarchitectures, and benchmarks demonstrates that the proposed model delivers very accurate reliability estimations (FIT rates) compared to statistically significant but slow fault injection campaigns at the microarchitecture level.Peer ReviewedPostprint (author's final draft
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