108,961 research outputs found
Sensitivity Analysis for a Scenario-Based Reliability Prediction Model
As a popular means for capturing behavioural requirements, scenariosshow how components interact to provide system-level functionality.If component reliability information is available, scenarioscan be used to perform early system reliability assessment. Inprevious work we presented an automated approach for predictingsoftware system reliability that extends a scenario specificationto model (1) the probability of component failure, and (2) scenariotransition probabilities. Probabilistic behaviour models ofthe system are then synthesized from the extended scenario specification.From the system behaviour model, reliability predictioncan be computed. This paper complements our previous work andpresents a sensitivity analysis that supports reasoning about howcomponent reliability and usage profiles impact on the overall systemreliability. For this purpose, we present how the system reliabilityvaries as a function of the components reliabilities and thescenario transition probabilities. Taking into account the concurrentnature of component-based software systems, we also analysethe effect of implied scenarios prevention into the sensitivity analysisof our reliability prediction technique
Reliability prediction in model driven development
Evaluating the implications of an architecture design early in the software development lifecycle is important in order to reduce costs of development. Reliability is an important concern with regard to the correct delivery of software
system service. Recently, the UML Profile for Modeling Quality of Service has defined a set of UML extensions to represent dependability concerns (including reliability) and other non-functional requirements in early stages of the software
development lifecycle. Our research has shown that these extensions are not comprehensive enough to support reliability analysis for model-driven software engineering,
because the description of reliability characteristics in this profile lacks support for certain dynamic aspects that are essential in modeling reliability. In this work, we define a profile for reliability analysis by extending the UML 2.0
specification to support reliability prediction based on scenario specifications. A UML model specified using the profile is translated to a labelled transition system (LTS), which is used for automated reliability prediction and identification of implied
scenarios; the results of this analysis are then fed back to the UML model. The result is a comprehensive framework for addressing software reliability modeling, including analysis and evolution of reliability predictions. We exemplify our approach using the Boiler System used in previous work and demonstrate
how reliability analysis results can be integrated into UML models
Validating Predictions of Unobserved Quantities
The ultimate purpose of most computational models is to make predictions,
commonly in support of some decision-making process (e.g., for design or
operation of some system). The quantities that need to be predicted (the
quantities of interest or QoIs) are generally not experimentally observable
before the prediction, since otherwise no prediction would be needed. Assessing
the validity of such extrapolative predictions, which is critical to informed
decision-making, is challenging. In classical approaches to validation, model
outputs for observed quantities are compared to observations to determine if
they are consistent. By itself, this consistency only ensures that the model
can predict the observed quantities under the conditions of the observations.
This limitation dramatically reduces the utility of the validation effort for
decision making because it implies nothing about predictions of unobserved QoIs
or for scenarios outside of the range of observations. However, there is no
agreement in the scientific community today regarding best practices for
validation of extrapolative predictions made using computational models. The
purpose of this paper is to propose and explore a validation and predictive
assessment process that supports extrapolative predictions for models with
known sources of error. The process includes stochastic modeling, calibration,
validation, and predictive assessment phases where representations of known
sources of uncertainty and error are built, informed, and tested. The proposed
methodology is applied to an illustrative extrapolation problem involving a
misspecified nonlinear oscillator
Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations
To perform uncertainty, sensitivity or optimization analysis on scalar
variables calculated by a cpu time expensive computer code, a widely accepted
methodology consists in first identifying the most influential uncertain inputs
(by screening techniques), and then in replacing the cpu time expensive model
by a cpu inexpensive mathematical function, called a metamodel. This paper
extends this methodology to the functional output case, for instance when the
model output variables are curves. The screening approach is based on the
analysis of variance and principal component analysis of output curves. The
functional metamodeling consists in a curve classification step, a dimension
reduction step, then a classical metamodeling step. An industrial nuclear
reactor application (dealing with uncertainties in the pressurized thermal
shock analysis) illustrates all these steps
Reliability Analysis of Concurrent Systems using LTSA
The analysis for software dependability is considered an important task within the software engineering life cycle. However, it is often impossible to carry out this task due to the complexity of available tools, lack of expert personnel and time-to-market pressures. As a result, released software versions may present unverified dependability properties subjecting customers to blind software reliability assessment. In particular, concurrent systems present certain behaviour that require a more complex system analysis not easily grasped at system design and architecture level
Integrating and Ranking Uncertain Scientific Data
Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates
Error by design: Methods for predicting device usability
This paper introduces the idea of predicting ‘designer error’ by evaluating devices using Human Error Identification (HEI) techniques. This is demonstrated using Systematic Human Error Reduction and Prediction Approach (SHERPA) and Task Analysis For Error Identification (TAFEI) to evaluate a vending machine. Appraisal criteria which rely upon user opinion, face validity and utilisation are questioned. Instead a quantitative approach, based upon signal detection theory, is recommended. The performance of people using SHERPA and TAFEI are compared with heuristic judgement and each other. The results of these studies show that both SHERPA and TAFEI are better at predicting errors than the heuristic technique. The performance of SHERPA and TAFEI are comparable, giving some confidence in the use of these approaches. It is suggested that using HEI techniques as part of the design and evaluation process could help to make devices easier to use
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