London,

This paper reviews the use of Bayesian Networks (BNs) in predicting software defects and software reliability. The approach allows us to incorporate causal process factors as well as combine qualitative and quantitative measures, hence overcoming some of the well-known limitations of traditional software metrics methods. The approach has been used by organisations such as Motorola, Siemens and Philips who have reported accurate predictions. However, one of the impediments to more widespread use of BNs for this type of application was that, traditionally, BN tools and algorithms suffered from an obvious “Achilles ’ Heel ” – they were not able to handle continuous nodes properly, if at all. This forced modellers to have to pre-define discretisation intervals in advance and resulted in very inaccurate predictions where the range, for example, of defect counts was large. Fortunately, recent advances in BN algorithms now enable us to use so-called dynamic discretisation for any continuous nodes. This results in significantly improved accuracy for defects and reliability prediction type models. 1