As is clear to any user of software, quality control of software has not reached the same levels of sophistication as it has with traditional manufacturing. In this paper we argue that this is because insufficient thought is being given to the methods of reasoning under uncertainty that are appropriate to this domain. We then describe how we have built a large-scale Bayesian network to overcome the difficulties that have so far been met in software quality control. This exploits a number of recent advances in tool support for constructing large networks. We end the paper by describing how the network was validated and illustrate the range of reasoning styles that can be modelled with this tool.