Two types of models namely, the causal type and the data analysis type are complimentary. The data analysis models (which commonly relate measures of software metrics to fault-proneness) are becoming common today while much work needs to be done regarding the causal models. Causal models can be divided into models of faults and fault detection while the other type directly models the probabilities of state-space. The strength of data analysis models is its ease of use and availability of case study applications. However, there is still need for more data analysis model applications for large scale OO systems. This situation is in the case of causal type models even worse. On the other hand, the issue of maintainability is hardly jointly studied with reliability and this works hopes to unite our modeling efforts for both of these software qualities. We briefly note that causal models are based on both topologicalinformation, and hence on modern partitioning techniques, and the information on independent components. Data analysis models consider these indirectly through metrics.

We will invite position papers on the following topics with emphasis on OO and component based systems.

• What is new in causal based models for reliability and maintainability
• What is new in data analysis models
• What are the relationships between the two types of models and how can we use these jointly
• What are the relationships between reliability and maintainability
• Any others

Co-Chairs:

Prof. K. Ponnambalam
Department of Systems Design Engineering
University of Waterloo
ponnu@uwaterloo.ca

Dr. Brian Stacey
Nortel Networks
bstacey@nortelnetworks.com