BBN-based software project risk management

Fan, Chin-Feng; Yu, Yuan-Chang

doi:10.1016/j.jss.2003.12.032

Cited by 132 publications

(74 citation statements)

References 9 publications

(12 reference statements)

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“…BBN becomes not only a powerful tool for knowledge representation but reasoning under conditions of uncertainty [16], frequently dealing with real-world problems such as building medical diagnostic systems, forecasting, and manufacturing process control for several decades [17]. Nowadays BBN has been extended to other applications including software risk management [18], ecosystem and environmental management [19], and transportation [20]. There is a great impact of key events on long-term transport mode choice decisions using Bayesian belief network, precisely Bayesian decision network, for the exploration of the suggested formalism in measuring, analyzing, and predicting dynamic travel mode choice in relation to key events and critical incidents [21].…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

Airlines Content Recommendations Based on Passengers' Choice Using Bayesian Belief Networks

Chen¹,

Huang²,

Chen³

et al. 2017

Bayesian Inference

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Faced with the increasingly fierce competition in the aviation market, the strategy of consumer choice has gained increasing significance in both academia and practice. As ever-increasing travel choices and growing consumer heterogeneity, how do airline companies satisfy passengers' needs? With a vast amount of data, how do airline managers combine information to excavate the relationship between independent variables to gain insight about passengers' choices and value system as well as determining best personalized contents to them? Using the real case of China Southern Airlines, this paper illustrates how Bayesian belief network (BBN) can enable airlines dynamically recommend relevant contents based on predicting passengers' choice to optimize the loyalty. The findings of this study provide airline companies useful insights to better understand the passengers' choices and develop effective strategies for growing customer relationship.

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Section: Bayesian Belief Networkmentioning

confidence: 99%

Airlines Content Recommendations Based on Passengers' Choice Using Bayesian Belief Networks

Chen¹,

Huang²,

Chen³

et al. 2017

Bayesian Inference

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show abstract

“…Fan and Yu [8], Fenton et al [9] and Hu et al [13] modeled software processes to support risk management. Fan and Yu [8] built a model capable of predicting potential risks, identifying the source of risks, and supporting dynamic resource adjustment. [9] showed how to use a Bayesian network to predict software defects and perform "what if" scenarios.…”

Section: Overview Of Bayesian Network Applied To Software Engineeringmentioning

confidence: 99%

“…It has been applied in several context of software engineering such as risk management [8,9,15,13], quality prediction [1,14] and process management [19].…”

Section: Introductionmentioning

confidence: 99%

A Method to Build Bayesian Networks based on Artifacts and Metrics to Assess Agile Projects

Willamy¹,

Nunes²,

Perkusich

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Managing software development projects is a complex task because it requires organizing and monitoring several activities. Recently, in order to assist on software projects management, artifact-based models were proposed in the literature. However, the current solutions do not present means to monitor projects health and assist on decisions making. Due to the recent popularization of agile methods, they are the units of study of this research. In this work, we present a method to build artifact and measurementbased models to assess agile projects health. We applied the method to build a generic model based on industrys best practice. We defined the models artifacts and metrics based on findings of a literature review and the assistance of an expert. For each models artifact, we applied the Goal-Question-Metric paradigm to define the metrics. Afterwards, from the GQM meta-model, we constructed a Bayesian network. We validated the model with simulated scenarios. Given the successful results, we concluded that the method and model are promising.

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“…Few specific methods are able to model risk correlations with a network structure. Several papers on the application of the Bayesian belief network (BBN) have appeared in recent years in the field of project risk management (Fan and Yu 2004, Lee et al 2008, Trucco et al 2008), which could model risk interrelations, from multiple inputs to multiple outputs. Nevertheless, BBN demands oriented links, is inherently acyclic, and hence does not easily model the loop phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Dealing with project complexity by matrix-based propagation modelling for project risk analysis

Fang

Marle

2013

Journal of Engineering Design

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BBN-based software project risk management

Cited by 132 publications

References 9 publications

Airlines Content Recommendations Based on Passengers' Choice Using Bayesian Belief Networks

Airlines Content Recommendations Based on Passengers' Choice Using Bayesian Belief Networks

A Method to Build Bayesian Networks based on Artifacts and Metrics to Assess Agile Projects

Dealing with project complexity by matrix-based propagation modelling for project risk analysis

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