A HYBRID SOFTWARE COST ESTIMATION APPROACH UTILIZING DECISION TREES AND FUZZY LOGIC
Abstract
Software cost estimation (SCE) is one of the critical activities in software project management. During the past decades various models have been proposed for SCE. However, developing accurate and useful models is limited in practice despite the considerable financial gain they could offer to software stakeholders. Traditional techniques, such as regression, by-analogy and machine learning, face the difficulty of handling the dynamic nature of the software process and the problematic nature of the public data available. This paper addresses the issue of SCE proposing an alternative approach that combines robust decision tree structures with fuzzy logic. Fuzzy decision trees are generated using the CHAID and CART algorithms in a systematic manner, while development effort is treated as the dependent variable against two subsets of factors: The first contains selected attributes from the ISBSG, COCOMO and DESHARNAIS datasets and the second contains a subset of the available factors that can be measured early in the development cycle. The association rules obtained from the trees are then merged and defuzzified through a Fuzzy Implication System (FIS). The fuzzy framework is utilized to perform effort estimations. Experimental results indicate that the proposed approach is promising as it yields quite accurate estimations in most dataset cases considered. Finally, our evaluation suggests that accurate estimations may be produced, even when using only a small set of factors that can be measured early in the development cycle, thus increasing the practical value of the proposed cost model.
