Identification of fuzzy models of software cost estimation

Abstract

Software cost estimation is one of the most critical tasks in managing software projects. Development costs tend to increase with project complexity, and hence accurate cost estimates are highly desired during the early stages of development. An important objective of the software engineering community has been to develop useful models that constructively explain the software development life-cycle and accurately estimate the cost of software development. Currently used software development effort estimation models such as, COCOMO and Function Point Analysis, do not consistently provide accurate project cost and effort estimates. This is often because important project data, available at the time of modeling, are often vague, imprecise, and incomplete. Traditionally used cost estimation models cannot utilize such vague yet important information in their models. Fuzzy logic-based cost estimation models are more appropriate when vague and imprecise information is to be accounted for. Such models usually rely on expert knowledge, which is however, often too general to fit a particular data set because different data sets have different characteristics. We present an innovative fuzzy identification cost estimation modeling technique to deal with linguistic data, and automatically generate fuzzy membership functions and rules. A case study based on the COCOMO81 database compared the proposed model with all three COCOMO models, i.e., Basic, Intermediate, and Detailed. It was observed that the fuzzy identification model provided significantly better cost estimations than the three COCOMO models.