Her main research interests include model based performance analysis and feedback on software architectures under uncertainties and optimisation of largescale software systems. Software and systems modeling sosym under revision. An integrated real options framework for modelbased. Grunske, modelbased performance analysis of software architectures under uncertainty. A container is a softwarebased packaging and distribution tool that collects all elements and dependencies of a linuxbased application. Pestppopt solves a sequential linear programming slp problem and also implements optional efficient, onthefly without user intervention firstorder, secondmoment fosm uncertainty techniques to. Causal explanations for model interpretation under. The uptake of bps in current building design projects is limited. Previous work has tackled the problem of model based performance and reliability evaluation of software systems in presence of uncertainties 9, 12, 48, 49. As a result, many softwarebased systems fail to meet. Failure probability analysis of the multidisciplinary blade system is performed using the monte carlo simulation and the surrogate model. This years main theme is performance engineering under uncertainty. Performance analysis of various activation functions in.
Oct 27, 2019 we also found that, analogous to standard supervised learning tasks, specialpurpose model architectures may improve the performance of neural explanation models in images, and that the bootstrap resampled uncertainty estimates for the importance scores of an explanation model are significantly correlated with cxplains ability to accurately. Eng, software systems engineering, royal melbourne institute of technology 2002 s. It should be emphasized that data used to evaluate the model performance also include uncertainty. Management study models performance assessment metrics and. The performance of robustnessbased design can be defined by the mean and. When dealing with uncertainty, many approaches are based on sensitivity analysis techniques that aim to identify the parameter ranges affecting the software nonfunctional properties, e. A containerized mesoscale model and analysis toolkit to. A number of software products have been released under the nasa open source agreement on the nasa github page. Andre van hoorn is a member of the institute of software technology at the university of stuttgart, germany.
Prognostics model library and prognostics algorithm. Process safety and environmental protection, 1, pp. International conference on performance engineering. The ccr and sandia have a long and distinguished history of leadership in computational science and engineering, including massively parallel computation, uncertainty quantification, mathematical optimization, scalable solvers, software toolkits, and scientific software engineering, to name a few.
Modelbased performance analysis of software architectures under uncertainty, proceedings of international conference on the quality of software. Technical reports simulation based engineering lab uw. Models performance assessment metrics and uncertainty analysis. Multifidelity model construction building surrogate, hierarchical or competing models exploiting structure quantification of uncertainty and model fidelity how good is a model for a given purpose multifidelity model management which model to use when. Methodology for managing the effect of uncertainty in. First, we describe different approachesto build the architecture based software reliability model and to estimate parameters. To determine whether the requirements are achieved, it is necessary to quantitatively evaluate quality attributes on the architecture model. Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machinelearning models. Modelbased software performance analysis introduces performance concerns in the scope of software modeling, thus allowing the developer to carry on. Modelbased performance analysis of software architectures under uncertainty article pdf available june 20 with 757 reads how we measure reads. Focused on a novelproposed dynamic sdn framework, a gametheoretic model is presented to analyze its security performance. This article presents an approach that combines the active global kriging method and multidisciplinary strategy to investigate the problem of evidence based multidisciplinary design optimization.
Analysis and prediction crosstier mapping and uncertainty. Modelbased software performance analysis guide books. Software architectures general terms design, economics, theory keywords software engineering decision analysis 1. A generalized software reliability model considering. May 31, 2018 designing a software architecture that satisfies all quality requirements is a difficult task. This work exploits the rapid emergence of software container technology to produce a transformative research and education environment. All of these techniques are brought together in a single, easy. Assessing uncertainty in reliability of componentbased. Software architecture for the cloud a roadmap towards control. Dealing with uncertainties in the performance modelling of software systems. An analysis of a primaldual interior point method for computing frictional contact forces in a differential inclusionbased approach for multibody dynamics, l.
The role of verification, validation, and uncertainty quantification vvuq in computational science and engineering has increased significantly in recent years. Evidencebased multidisciplinary design optimization with the. Productfocused software process improvement 14th international conference, profes 20, proceedings. Dealing with and understanding the effects of uncertainty are important tasks for the control engineer. Analytical modeling of performance indices under epistemic. Probabilistic models are commonly used to evaluate quality attributes, such as reliability, availability, safety and performance of softwareintensive systems. The probabilistic formulation of parameter uncertainties is sampled using a monte carlo based approach to systematically assess the robustness of a software system under uncertainty. The purpose of this article is to investigate the cognitive processes involved in decision making under partial uncertainty.
Darren elliott tecolote research charles hunt nasa ooecad. Optimization under moment, robust, and datadriven models of uncertainty by xuanvinhdoan b. Exploiting traceability uncertainty between software. Modelbased design and analysis of concurrent and adaptive. An efficient method for reliability analysis under epistemic uncertainty based on evidence theory and support vector regression 17 july 2015 journal of engineering design, vol. Model based performance analysis of software architectures under uncertainty. Previous work has tackled the problem of modelbased performance and reliability evaluation of software systems in presence of uncertainties 9, 12, 48, 49. Degrees of freedom in componentbased software architecture models. Center for computing research sandia national laboratories. Modelbased systems for engineering and architectures is an analysis capability that integrates modeling, mission simulation, data analysis, computational technologies, data management, and visualization to help formulate and validate advanced instruments and associated payload systems.
Embedded systems and high performance computing epic. Over the past two decades, our lab has introduced methods for design under uncertainty to mitigate the effects of uncertainty, reduce the potential risks, and improve the product performance with low cost. Models performance assessment metrics and uncertainty analysis iii. Introduction performance is a quality attribute that, in spite of being critical to a large number of software systems, is often not appropriately addressed. It is particularly present in the early stages of software development when an organisation needs to make strategic decisions about which it. In software performance, one of the seminal works in this direction is 35, where the concepts of uncertainty, performance conditions and implications. As hardware and software characteristics become uncertain i. Modelbased performance analysis of software architectures under uncertainty. A good evaluation process should have proper answers for these questions. To fill a need for riskbased environmental management optimization, we have developed pestppopt, a modelindependent tool for resource management optimization under uncertainty. While some critical aspects such as uncertainty have already been taken into account. State machines in form of lts labelled transition systems. The effective integration of uncertainty analysis ua in building performance simulation bps for design information and quality assurance is of high importance and will be discussed further on.
Cost and schedule uncertainty analysis of growth in support of jcl 2014 nasa cost symposium larc, august, 2014 presenters. Robustnessbased design optimization under data uncertainty. Performancebased selection of software and hardware. The research method used in this work is based on the experimental software engineering principles which is. Modelbased design and analysis of concurrent and adaptive software. Modelbased software performance analysis springerlink. Dealing with uncertainties in the performance modelling of. This article presents an approach that combines the active global kriging method and multidisciplinary strategy to investigate the problem of evidencebased multidisciplinary design optimization. Analytical modeling of performance indices under epistemic uncertainty. The dakota toolkit for parallel optimization and uncertainty. Uncertainty multidisciplinary design optimization methods aim at efficiently organizing not only the different disciplinary analyses, the uncertainty propagation, and the. Modelbased performance analysis of software architectures under.
The meshbased method supported by freeform deformation is proposed. This software is being used at sandia national laboratories for analyzing results from our computer performance modeling analysis, but these codes can be used for any type of analysis where database storage and analysis is desired. Section 7 offers an overview of a guide for uncertainty management in software projects and finally section 8 contains the conclusion. Quantification for modelbased performance and reliability evaluation of software architectures in presence of uncertainties is provided in 7, 12, however no reduction of uncertainty is tackled. Trubiani c, meedeniya i, cortellessa v, aleti a and grunske l model based performance analysis of software architectures under uncertainty proceedings of the 9th international acm sigsoft conference on quality of software architectures, 6978. Quantification for model based performance and reliability evaluation of software architectures in presence of uncertainties is provided in 7, 12, however no reduction of uncertainty is tackled. The validity of the model characterized into three categories. Estimating and understanding architectural risk micro50, october 1418, 2017. Uncertainty analysis in building performance simulation for. The aim of this study is to analyze the performance of generalized mlp architectures which has backpropagation algorithm using various different activation functions for the neurons of hidden and output layers.
Combined with a userspecified risk value, the constraint uncertainty estimates are used to form chanceconstraints for the slp solution process, so that any optimal solution includes contributions from model input and observation uncertainty. Uncertainty analysis in building performance simulation. Design under uncertainty ideal lab at northwestern. Software engineering institute, l3 communications eits, and the jet propulsion laboratory jpl have collaborated in a use of modelbased engineering for the national aeronautics and space administration nasa software assurance research program sarp project named modelbased software assurance with the sae architecture analysis. The global kriging model is constructed by introducing a socalled learning function and using actively selected samples in the entire optimization space. Multifidelity modeling for uncertainty quantification and. State machines in form of lts labelled transition systems lightweight tool support. Model checking in form of cra compositional reachability analysis. Ieee transactions on software engineering tse under revision. An efficient method for uncertainty propagation in robust. Management study models performance assessment metrics. The complexity of managing performancerelated concerns under uncertainty is starting to overwhelm even the capabilities of large engineering teams. At early design phases, taking into account uncertainty in the optimization of a multidisciplinary system is essential to assess the optimal characteristics and performance. In the proceedings of the th nondeterministic approaches conference colocated with the 52nd aiaaasmeasceahsasc structures, structural dynamics and materials conference, denver, co, april 4 7, 2011.
Users employ the software products at their own risk. Multidisciplinary reliability analysis of turbine blade. From a policy perspective, the value of a modelbased analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. The approach is best suited for componentbased software architectures. An integrated real options framework for modelbased identi. Validation of a steadystate magic formula tire in chrono with a commercial software implementation, j. Examples of such methods include, the analysis of the propagation of parameter uncertainties e. Uncertainty is particularly critical in the performance domain when it relates to values of parameters such as workload, operational profile, resource demand of services, service time of hardware. Grunske, model based performance analysis of software architectures under uncertainty. Components encapsulate functionality that can be independently reused, and thus componentbased software architectures provide degrees of freedom to be exploited by our ap. A containerized mesoscale model and analysis toolkit to accelerate classroom learning, collaborative research, and uncertainty quantification next article. Modelbased performance prediction in software development. In this way, a single answer that includes uncertainty is yielded from the modeling analysis. Ua can, for instance, provide information about the reliability and influence of design parameters, with respect to the overall design.
Identifying and handling uncertainties in the feedback. Security evaluation of sdn architectures is of critical importance to develop robust systems and address attacks. Home conferences comparch proceedings qosa 14 dealing with uncertainties in the performance modelling of software systems. Model based design integrate modelling into the software lifecycle.
Performance analysis is often conducted before achieving full knowledge of a software system, in other words under a certain degree of uncertainty. Reducing the effects of some forms of uncertainty initial conditions, lowfrequency disturbances without catastrophically increasing the effects of other dominant forms sensor noise, model uncertainty is the primary job of the feedback control system. Costbenefit analysis under uncertainty before considering early requirements and architecture decision problems, we. Automatically improve software architecture models for. Jun 01, 2017 a containerized mesoscale model and analysis toolkit to accelerate classroom learning, collaborative research, and uncertainty quantification. An empirical analysis of requirements uncertainty, task uncertainty and software project performance 1ping lu, 2xuemin song, 3yinqiu song 1school of management, university of science and technology of chinaschool of management, university of science and technology of china, no.
In case of unsatisfactory results, we introduce refactoring actions aimed at generating new software architectural models that better tolerate the uncertainty of. A generalized software reliability model considering uncertainty and dynamics in development. Apparently, this opinion reflects a disenchantment with stimulusresponse conditioning theories and a lack of development of cognitive theories of decision making under uncertainty. The dakota toolkit for parallel optimization and uncertainty analysis. Evaluating probabilistic models with uncertain model. While software architecture performance analysis is a wellstudied field, it is less understood how the analysis results i. Modern systems are subject to multiple sources of uncertainty due to openness, heterogeneity, versatility, and variability. Aldeida aleti, barbora buhno v a, anne koziolek, lars grunske, and indika meedeniya, a systematic survey on software architecture optimization methods.
Portfolio selection under model uncertainty 3 only partial moment information of underlying probability measure is available. Algorithms and architectures for energyefficient, faulttolerant, and secure design of. Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization building performance simulation bps uses computerbased models that cover performance aspects such as energy consumption and thermal comfort in buildings. The weather research and forecasting wrf model anchors a set of linked linux based containers, which include software to initialize and run the model, to analyze results, and to serve output to collaborators. The probabilistic formulation of parameter uncertainties is sampled using a monte carlobased approach to systematically assess the robustness of a software system under uncertainty. Model parameter estimation and uncertainty analysis. Researchers have tackled this problem by including uncertainties in the. The accuracy of the evaluation results depends on a number of system properties which have to be estimated, such as environmental factors or system usage. An empirical analysis of requirements uncertainty, task. Trubiani c, meedeniya i, cortellessa v, aleti a and grunske l modelbased performance analysis of software architectures under uncertainty proceedings of the 9th international acm sigsoft conference on quality of software architectures, 6978. Through the numerical simulation, it is found that the failure probability increases as the blade shape uncertainty becomes larger. Nasa does not assume any liability for the use of the software or any system developed using the software.
Software architectures of components, translatable to models relatively easy to learn and use. Uncertainty, risk, and information value in software. Introduction the world is undergoing rapid changes. Modelbased performance analysis of software architectures. Evidencebased multidisciplinary design optimization with.
A conceptual basis for uncertainty management in modelbased. Model uncertainty has been considered in deterministic approaches for a long time. Uncertainty quantification and model validation under epistemic uncertainty due to sparse andor imprecise data. Introduction uncertainty is inevitable in software engineering. Our proposed formulations deal with both sparse point and interval data without any assumption about probability distributions of the random variables.
Thus, the need to address performance concerns early during the software development process is fully acknowledged, and there is a growing interest in the research and software industry communities towards techniques, methods and tools that permit to manage system performance concerns as an integral part of software engineering. Designing a software architecture that satisfies all quality requirements is a difficult task. Automated improvement of software architecture models for. Security analysis of dynamic sdn architectures based on. Optimization under moment, robust, and datadriven models.
In icse 2009 workshop on modelbased methodologies for pervasive and embedded software mompes 2009, may 16, 2009, vancouver, canada. For experimental comparisons, bipolar sigmoid, unipolar sigmoid, tanh, conic section, and radial bases function rbf were used. Embedded systems and high performance computing epic lab. Modelling and analysing software requirements and architecture. Uncertainty handling in fault tree based risk assessment. Importantly it can do this even without a priori knowledge of an analytic model governing that uncertainty. Causal explanations for model interpretation under uncertainty. Architecture optimisation of embedded systems under. The performance of robustness based design can be defined by the mean and. Such problem assume a model exists to calculate the costs.