What is the point of improving maintenance processes if the most important asset, people, is not properly utilised? Knowledge of the product(s) maintained, maintenance processes and communications skills is very important for achieving quality software and for improving maintenance and development processes. We present CM/sup 3/: Maintainer's Education and Training-a maturity model for educating and training maintenance engineers. This model is the result of a comparative study of two industrial processes utilised at ABB, and of process models such as IEEE 1219, ISO/IEC 12207, CMM, People CMM, and TickIT.

Missing data are often encountered in data sets used to construct software effort prediction models. Thus far, the common practice has been to ignore observations with missing data. This may result in biased prediction models. The authors evaluate four missing data techniques (MDTs) in the context of software cost modeling: listwise deletion (LD), mean imputation (MI), similar response pattern imputation (SRPI), and full information maximum likelihood (FIML). We apply the MDTs to an ERP data set, and thereafter construct regression-based prediction models using the resulting data sets. The evaluation suggests that only FIML is appropriate when the data are not missing completely at random (MCAR). Unlike FIML, prediction models constructed on LD, MI and SRPI data sets will be biased unless the data are MCAR. Furthermore, compared to LD, MI and SRPI seem appropriate only if the resulting LD data set is too small to enable the construction of a meaningful regression-based prediction model.