6,713 research outputs found

    Predicting Software Suitability Using a Bayesian Belief Network

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    The ability to reliably predict the end quality of software under development presents a significant advantage for a development team. It provides an opportunity to address high risk components earlier in the development life cycle, when their impact is minimized. This research proposes a model that captures the evolution of the quality of a software product, and provides reliable forecasts of the end quality of the software being developed in terms of product suitability. Development team skill, software process maturity, and software problem complexity are hypothesized as driving factors of software product quality. The cause-effect relationships between these factors and the elements of software suitability are modeled using Bayesian Belief Networks, a machine learning method. This research presents a Bayesian Network for software quality, and the techniques used to quantify the factors that influence and represent software quality. The developed model is found to be effective in predicting the end product quality of small-scale software development efforts

    A Life Cycle Software Quality Model Using Bayesian Belief Networks

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    Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products

    Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox

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    The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment

    Wildfire may increase habitat quality for spring Chinook salmon in the Wenatchee River subbasin, WA, USA

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    Pacific Northwest salmonids are adapted to natural disturbance regimes that create dynamic habitat patterns over space and through time. However, human land use, particularly long-term fire suppression, has altered the intensity and frequency of wildfire in forested upland and riparian areas. To examine the potential impacts of wildfire on aquatic systems, we developed stream-reach-scale models of freshwater habitat for three life stages (adult, egg/fry, and juvenile) of spring Chinook salmon (Oncorhynchus tshawytscha) in the Wenatchee River subbasin, Washington. We used variables representing pre- and post-fire habitat conditions and employed novel techniques to capture changes in in-stream fine sediment, wood, and water temperature. Watershed-scale comparisons of high-quality habitat for each life stage of spring Chinook salmon habitat suggested that there are smaller quantities of high-quality juvenile overwinter habitat as compared to habitat for other life stages. We found that wildfire has the potential to increase quality of adult and overwintering juvenile habitat through increased delivery of wood, while decreasing the quality of egg and fry habitat due to the introduction of fine sediments. Model results showed the largest effect of fire on habitat quality associated with the juvenile life stage, resulting in increases in high-quality habitat in all watersheds. Due to the limited availability of pre-fire high-quality juvenile habitat, and increased habitat quality for this life stage post-fire, occurrence of characteristic wildfires would likely create a positive effect on spring Chinook salmon habitat in the Wenatchee River subbasin. We also compared pre- and post-fire model results of freshwater habitat for each life stage, and for the geometric mean of habitat quality across all life stages, using current compared to the historic distribution of spring Chinook salmon. We found that spring Chinook salmon are currently distributed in stream channels in which in-stream habitat for most life stages has a consistently positive response to fire. This compares to the historic distribution of spring Chinook, in which in-stream habitat exhibited a variable response to fire, including decreases in habitat quality overall or for specific life stages. This suggests that as the distribution of spring Chinook has decreased, they now occupy those areas with the most positive potential response to fire. Our work shows the potentially positive link between wildfire and aquatic habitat that supports forest managers in setting broader goals for fire management, perhaps leading to less fire suppression in some situations

    Multi-criteria decision analysis in Bayesian networks-Diagnosing ecosystem service trade-offs in a hydropower regulated river

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    The paper demonstrates the use of Bayesian networks in multicriteria decision analysis (MCDA) of environmental design alternatives for environmental flows (eflows) and physical habitat remediation measures in the Mandalselva River in Norway. We demonstrate how MCDA using multi-attribute value functions can be implemented in a Bayesian network with decision and utility nodes. An object-oriented Bayesian network is used to integrate impacts computed in quantitative sub-models of hydropower revenues and Atlantic salmon smolt production and qualitative judgement models of mesohabitat fishability and riverscape aesthetics. We show how conditional probability tables are useful for modelling uncertainty in value scaling functions, and variance in criteria weights due to different stakeholder preferences. While the paper demonstrates the technical feasibility of MCDA in a BN, we also discuss the challenge

    Practical guidelines for modelling post-entry spread in invasion ecology

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    In this article we review a variety of methods to enable understanding and modelling the spread of a pest or pathogen post-entry. Building upon our experience of multidisciplinary research in this area, we propose practical guidelines and a framework for model development, to help with the application of mathematical modelling in the field of invasion ecology for post-entry spread. We evaluate the pros and cons of a range of methods, including references to examples of the methods in practice. We also show how issues of data deficiency and uncertainty can be addressed. The aim is to provide guidance to the reader on the most suitable elements to include in a model of post-entry dispersal in a risk assessment, under differing circumstances. We identify both the strengths and weaknesses of different methods and their application as part of a holistic, multidisciplinary approach to biosecurity research

    Comparing student model accuracy with bayesian network and fuzzy logic in predicting student knowledge level

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    The use of computer has widely used as a tool to help student in learning, one of the computer application to help student in learning is in the form of Intelligent Tutoring System. Intelligent Tutoring System used to diagnose student knowledge state and provide adaptive assistance to student. However, diagnosing student knowledge level is a difficult task due to rife with uncertainty. Student Model is the key component in Intelligent Tutoring System to deal with uncertainty. Bayesian Network and Fuzzy Logic is the most widely used to develop student model. In this paper we will compare the accuracy of student model developed with Bayesian Network and Fuzzy Logic in predicting student knowledge level
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