In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration\r\nsoftware project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open\r\nsource projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining\r\ntechniques are used to classify software projects in terms of their development complexity. Data mining techniques are also used\r\nto study association between different software attributes and their relation to cost estimation. Results showed that finance metrics\r\nare usually the most complex in terms of code size and some other complexity metrics. Results showed also that games applications\r\nhave higher values of the SLOCmath, coupling, cyclomatic complexity, and MCDC metrics. Information gain is used in order to\r\nevaluate the ability of object-oriented metrics to predict software complexity. MCDC metric is shown to be the first metric in\r\ndeciding a software project complexity. A software project effort equation is created based on clustering and based on all software\r\nprojects� attributes. According to the software metrics weights values developed in this project, we can notice that MCDC, LOC,\r\nand cyclomatic complexity of the traditional metrics are still the dominant metrics that affect our classification process, while\r\nnumber of children and depth of inheritance are the dominant from the object-oriented metrics as a second level.
Loading....