Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Software inspection process
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Using simulation to build inspection efficiency benchmarks for development projects
Proceedings of the 20th international conference on Software engineering
COBRA: a hybrid method for software cost estimation, benchmarking, and risk assessment
Proceedings of the 20th international conference on Software engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Software Reliability Engineered Testing
Software Reliability Engineered Testing
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Assessing the Cost-Effectiveness of Inspections by Combining Project Data and Expert Opinion
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Software Product Quality Practices Quality Measurement and Evaluation using TL9000 and ISO/IEC 9126
STEP '02 Proceedings of the 10th International Workshop on Software Technology and Engineering Practice
Building large-scale Bayesian networks
The Knowledge Engineering Review
Learning probabilistic networks
The Knowledge Engineering Review
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Assessing and controlling software quality is still animmature discipline. One of the reasons for this is that many ofthe concepts and terms that are used in discussing and describingquality are overloaded with a history from manufacturingquality. We argue in this paper that a quite distinct approach isneeded to software quality control as compared withmanufacturing quality control. In particular, the emphasis insoftware quality control is in design to fulfil business needs,rather than replication to agreed standards. We will describehow quality goals can be derived from business needs. Followingthat, we will introduce an approach to quality control that usesrich causal models, which can take into account human as well astechnological influences. A significant concern of developing suchmodels is the limited sample sizes that are available for elicitingmodel parameters. In the final section of the paper we will showhow expert judgement can be reliably used to elicit parameters inthe absence of statistical data. In total this provides an agenda fordeveloping a framework for quality control in softwareengineering that is freed from the shackles of an inappropriatelegacy.