Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Software engineering (3rd ed.): a practitioner's approach
Software engineering (3rd ed.): a practitioner's approach
Object-oriented modeling and design
Object-oriented modeling and design
Object-oriented analysis and design with applications (2nd ed.)
Object-oriented analysis and design with applications (2nd ed.)
Software requirements & specifications: a lexicon of practice, principles and prejudices
Software requirements & specifications: a lexicon of practice, principles and prejudices
The Unified Modeling Language user guide
The Unified Modeling Language user guide
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Software metrics: success, failures and new directions
Journal of Systems and Software - Special issue on invited articles on top systems and software engineering scholars
On the criteria to be used in decomposing systems into modules
Communications of the ACM
Software Engineering
Introduction to Bayesian Networks
Introduction to Bayesian Networks
A Discipline of Programming
Learning probabilistic networks
The Knowledge Engineering Review
Principles of Program Design
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network engineering for complex belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Proceedings of the Conference on The Future of Software Engineering
Software Measurement: Uncertainty and Causal Modeling
IEEE Software
Probabilistic Modelling for Software Quality Control
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
New Directions in Measurement for Software Quality Control
STEP '02 Proceedings of the 10th International Workshop on Software Technology and Engineering Practice
Making Resource Decisions for Software Projects
Proceedings of the 26th International Conference on Software Engineering
Scenario-Based Assessment of Nonfunctional Requirements
IEEE Transactions on Software Engineering
Predicting software defects in varying development lifecycles using Bayesian nets
Information and Software Technology
Ontologies for probabilistic networks: a case study in the oesophageal-cancer domain
The Knowledge Engineering Review
Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
A Methodological Approach for the Effective Modeling of Bayesian Networks
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
WSEAS Transactions on Information Science and Applications
An application of Bayesian network for predicting object-oriented software maintainability
Information and Software Technology
High-throughput bayesian computing machine with reconfigurable hardware
Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays
The use of a Bayesian network for web effort estimation
ICWE'07 Proceedings of the 7th international conference on Web engineering
Development process of the operational version of PDQM
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Modeling web quality using a probabilistic approach: An empirical validation
ACM Transactions on the Web (TWEB)
Causal networks for risk and compliance: methodology and application
IBM Journal of Research and Development
A modular design of Bayesian networks using expert knowledge: Context-aware home service robot
Expert Systems with Applications: An International Journal
Modeling web-based applications quality: a probabilistic approach
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Context modeling with bayesian network ensemble for recognizing objects in uncertain environments
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Generalising event trees using bayesian networks with a case study of train derailment
SAFECOMP'05 Proceedings of the 24th international conference on Computer Safety, Reliability, and Security
Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector
Environmental Modelling & Software
Building an expert-based web effort estimation model using bayesian networks
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
Predicting web development effort using a bayesian network
EASE'07 Proceedings of the 11th international conference on Evaluation and Assessment in Software Engineering
Proceedings of the 34th International Conference on Software Engineering
Assessing practical usefulness and performance of the PREDIQT method: An industrial case study
Information and Software Technology
Quality, trust, and utility of scientific data on the web: towards a joint model
Proceedings of the 3rd International Web Science Conference
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Bayesian networks (BNs) model problems that involve uncertainty. A BN is a directed graph, whose nodes are the uncertain variables and whose edges are the causal or influential links between the variables. Associated with each node is a set of conditional probability functions that model the uncertain relationship between the node and its parents. The benefits of using BNs to model uncertain domains are well known, especially since the recent breakthroughs in algorithms and tools to implement them. However, there have been serious problems for practitioners trying to use BNs to solve realistic problems. This is because, although the tools make it possible to execute large-scale BNs efficiently, there have been no guidelines on building BNs. Specifically, practitioners face two significant barriers. The first barrier is that of specifying the graph structure such that it is a sensible model of the types of reasoning being applied. The second barrier is that of eliciting the conditional probability values. In this paper we concentrate on this first problem. Our solution is based on the notion of generally applicable “building blocks”, called idioms, which serve solution patterns. These can then in turn be combined into larger BNs, using simple combination rules and by exploiting recent ideas on modular and object oriented BNs (OOBNs). This approach, which has been implemented in a BN tool, can be applied in many problem domains. We use examples to illustrate how it has been applied to build large-scale BNs for predicting software safety. In the paper we review related research from the knowledge and software engineering literature. This provides some context to the work and supports our argument that BN knowledge engineers require the same types of processes, methods and strategies enjoyed by systems and software engineers if they are to succeed in producing timely, quality and cost-effective BN decision support solutions.