Abstract
Software maintenance engineers need tools to support their work. To make such tools relevant, they should provide engineers with quantitative input, as well as the knowledge needed to understand factors influencing maintenance activities. This article proposes an approach leading to multitechnique knowledge extraction and development of a comprehensive meta-model prediction system in the area of corrective maintenance. It dwells on elements of evidence theory and a number of fuzzy-based models. The models are developed using an evolutionary-based approach with different objectives applied to different subsets of data. Evidence theory–based Transferable Belief Model and belief function values assigned to generated models are used for reasoning purposes. The study comprises a detailed case for estimating the number of defects in a medical imaging system. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1093–1115, 2005.
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