This paper reviews the use of Bayesian networks (BNs) in predicting software reliability and software defects. The approach allows analysts 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 and reported on by organizations such as Motorola, Siemens, and Philips. 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 modelers to have to predefine discretization intervals in advance and resulted in inaccurate predictions where the range, for example, of defect counts was large. Fortunately, recent advances in BN algorithms now make it possible to perform inference in BNs with continuous nodes, without the need to pre-specify discretization levels. Using such ‘dynamic discretization’ algorithms results in significantly improved accuracy for reliability and defects prediction type models.
This study examined the challenges of building maintenance in Nigeria. The study embarked on physical inspection of the facilities of some public and private buildings, identified defects in the buildings, determined the causes of the defect and proffered remedies for them. Data for the study were collected through well-structured questionnaire administered to building industry professionals. Data collected were analyzed using frequency distribution tables and relative significance index. The findings revealed that in the level of dilapidation of services in the facilities, kerosene cooking system ranked first (68% significance) followed by flush toilet (66%), while the pail system ranked least with (50%) significance. Considering the severity of defects in facilities, peeling of wall surface ranked first (50.8% significance) while foundation failure and sagging of beams were ranked least with (42.8%). The causes of defects in the facilities were investigated and the use of untested or inferior materials (56.8%) was the most devastating factor. Availability of qualified and competent construction industry professionals was generally believed to be the most significant factor that would impact on the drive to achieve quality of maintenance operations in Nigeria.