Capability Maturity Model Integrated (CMMI) developed by Software Engineering Institute of Carnegie Mellon University is aimed to improve the productivity of software development. From CMM to CMMI, they have been adopted worldwide. But the large numbers of unsuccessful and/or painful implementations have proved these implementations easier said than done. The software industry in Taiwan faces the challenge of global competition. Continuous improvement of productivity becomes inevitable. To achieve this goal, recently, government and non-government organizations together promote the CMMI. This thesis focuses on the Decision Analysis and Resolution of CMMI Level 3. Through the design of related methods and the development of software tools, the decision process can be more structured and more effective. Potential applications include evaluation and selection of software and hardware, outsourcing partner, tools, risk resolution alternatives or people. The typical Go/No-Go decision can also fit into it. The decision analysis methods included in this research are Multi-attribute Decision Making and Analytic Hierarchy Process(AHP). In addition, alternative screening and group decision making can be integrated into the above methods.

A fusion system sometimes requires the capability to represent the temporal changes of uncertain sensory information in dynamic and uncertain situation. A Bayesian Network can construct a coherent fusion structure with the hypothesis node which cannot be observed directly and sensors through a number of intermediate nodes that are interrelated by cause and effect. In some BN applications for observing a hypothesis node with the number of participated sensors, rank and select the appropriate options (different combination of sensors allocation) in the decision-making is a challenging problem. By user interaction, we can acquire more and useful information through multi-criteria decision aid (MCDA) as semi-automatically decision support. So in this study, Multi Attribute Decision Making (MADM) techniques as TOPSIS, SAW, and Mixed (Rank Average) for decision-making as well as AHP and Entropy for obtaining the weights of indexes have been used. Since MADM techniques have most probably different results according to different approaches and assumptions in the same problem, statistical analysis done on them. According to results, the correlation between applied techniques for ranking BN options is strong and positive because of the close proximity of weights suggested by AHP and Entropy. Mixed method as compared to TOPSIS and SAW is ideal techniques; moreover, AHP is more acceptable than Entropy for weighting of indexes.