| CASE (Computer Aided Software Engineering) tools have played a critical role in improving software productivity and quality by assisting tasks in software development processes since the 1970's. Several parametric software cost models adopt "use of software tools" as one of the environmental factors that affect software development productivity. However, most software development teams use CASE tools that are assembled over time and adopt new tools without establishing formal evaluation criteria. Several software cost models assess the productivity impacts of CASE tools based just on breadth of tool coverage without considering other productivity dimensions such as degree of integration, tool maturity, and user support. This dissertation provides an extended set of tool rating scales based on the completeness of tool coverage, the degree of tool integration, and tool maturity/user support. It uses these scales to refine the way in which CASE tools are effectively evaluated within COCOMO (COnstructive COst MOdel) II. In order to find a best fit of weighting values for the extended set of tool rating scales in the extended research model, a Bayesian approach is adopted to combine two sources of (expert-judged and data-determined) information to increase prediction accuracy. The research model using the extended three TOOL rating scales is validated by using cross-validation methodologies such as data splitting and bootstrapping. |
