A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Software Quality: The Elusive Target
IEEE Software
Basic Concepts and Taxonomy of Dependable and Secure Computing
IEEE Transactions on Dependable and Secure Computing
Toward Econometric Models of the Security Risk from Remote Attack
IEEE Security and Privacy
Towards Reusable Measurement Patterns
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Constructing a Bayesian Belief Network to Predict Final Quality in Embedded System Development
IEICE - Transactions on Information and Systems
Predicting Software Suitability Using a Bayesian Belief Network
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Software Testing, Verification & Reliability - UKTest 2005: The Third U.K. Workshop on Software Testing Research
A literature survey of the quality economics of defect-detection techniques
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
A life cycle software quality model using bayesian belief networks
A life cycle software quality model using bayesian belief networks
An Integrated Approach to Quality Modelling
WoSQ '07 Proceedings of the 5th International Workshop on Software Quality
Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
Tool Support for Continuous Quality Control
IEEE Software
A Comprehensive Model of Usability
Engineering Interactive Systems
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Quality models in practice: A preliminary analysis
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Software quality models: purposes, usage scenarios and requirements
WOSQ'09 Proceedings of the Seventh ICSE conference on Software quality
Security vulnerabilities in software systems: a quantitative perspective
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
The use of application scanners in software product quality assessment
Proceedings of the 8th international workshop on Software quality
8th international workshop on software quality (WoSQ)
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
ACM SIGSOFT Software Engineering Notes
The quamoco product quality modelling and assessment approach
Proceedings of the 34th International Conference on Software Engineering
Using evidential reasoning to make qualified predictions of software quality
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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Context: Software quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activity-based quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty. Objective: The qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from an assessment goal and a quality model. Method: The four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed. Results: The approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions. Conclusion: The approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.