Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Software Metrics: A Rigorous and Practical Approach
Software Metrics: A Rigorous and Practical Approach
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Building large-scale Bayesian networks
The Knowledge Engineering Review
Making Resource Decisions for Software Projects
Proceedings of the 26th International Conference on Software Engineering
IEEE Software
Scenario-Based Assessment of Nonfunctional Requirements
IEEE Transactions on Software Engineering
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Predicting software defects in varying development lifecycles using Bayesian nets
Information and Software Technology
Predicting football results using Bayesian nets and other machine learning techniques
Knowledge-Based Systems
Predicting object-oriented software maintainability using multivariate adaptive regression splines
Journal of Systems and Software
Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Quality, productivity and economic benefits of software reuse: a review of industrial studies
Empirical Software Engineering
Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods
IEEE Transactions on Software Engineering
Software maintenance project delays prediction using Bayesian Networks
Expert Systems with Applications: An International Journal
Predicting defect-prone software modules using support vector machines
Journal of Systems and Software
Improvement of causal analysis using multivariate statistical process control
Software Quality Control
On the effectiveness of early life cycle defect prediction with Bayesian Nets
Empirical Software Engineering
Using formal specifications to support testing
ACM Computing Surveys (CSUR)
Visibility of Journals for Journal of Visualization
Journal of Visualization
Probabilistic estimation of software size and effort
Expert Systems with Applications: An International Journal
The use of a Bayesian network for web effort estimation
ICWE'07 Proceedings of the 7th international conference on Web engineering
Modular analysis and modelling of risk scenarios with dependencies
Journal of Systems and Software
Goal-driven evaluation of process fragments using weighted dependency graphs
Proceedings of the 2011 International Conference on Software and Systems Process
An iterative semi-supervised approach to software fault prediction
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
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
Finding upper bounds for software failure probabilities – experiments and results
SAFECOMP'05 Proceedings of the 24th international conference on Computer Safety, Reliability, and Security
Predicting web development effort using a bayesian network
EASE'07 Proceedings of the 11th international conference on Evaluation and Assessment in Software Engineering
A model to detect problems on scrum-based software development projects
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Software defect prediction using Bayesian networks
Empirical Software Engineering
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Software measurement has the potential to play an important role in risk management during product development. Metrics incorporated into predictive models can give advanced warning of potential risks. However, the common approach of using simple regression models, notably to predict software defects, can lead to inappropriate risk management decisions. These naïve models should be replaced with predictive models incorporating genuine cause-effect relationships. The authors show how to build these models using Bayesian networks, a powerful graphical modeling technique for software quality risk management that is providing accurate predictions of software defects in a range of real projects. As well as their use for prediction, Bayesian networks can also be used for performing a range of"what if" scenarios to identify potential problems and possible improvement actions.