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Effective software maintainability is one of the most significant and challenging activity in the field of component based software. Several maintainability models are proposed by the researchers to reduce the maintenance cost, to improve the quality and life span of the software product. The proposed model will assist the software designers to develop maintainable softwares. This paper discusses a maintainability model, which selects four crucial factors that highly affect maintainability of component based software system. Soft computing techniques are employed to demonstrate strong correlation of these factors with maintainability. MATLAB’s Fuzzy logic toolbox is used for predicting the maintainability level of component (such as Excellent, Fair, Good, Bad and worst). Data generated by fuzzy model are provided as input to artificial neural network model. Experimental results shows mean absolute error (MAE) to be .028 and Relative Error (RE) to be .045.To further improve the performance of the model; neuro-fuzzy tool was employed. With the use of self learning capability of this tool, MAE and RE are now improved to the value .0029 and .039. It means that the model was sound enough to provide satisfactory outcomes in comparison to neural network.

Maintenance is one of the extremely important and tricky missions in the area of component-based software. Numerous maintainability models are proposed by the scientist and researchers, to reduce the cost of maintenance, for improving the excellence and life period of a component-based system. Various quality models have been discussed briefly to show importance of maintainability. This research will facilitate the software designer to assemble maintainable component-based softwares. The proposed configuration confers a fuzzy-based maintainability model that chooses four fundamental features that enormously influence maintainability of component-based software system, i.e., Document Quality, Testability, Coupling, and Modifiability (DTMC). MATLAB's fuzzy logic toolbox is utilized to implement this configuration and output values are confirmed using center of gravity formula, as we have taken centroid defuzzification method. For a particular set of input, output provided by the model is 0.497 and output value from center of gravity formula comes up to be 0.467 which is around the value specified by the model.

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