Domain-based effort distribution model for software cost estimation

In software cost estimation, effort allocation is an important and usually challenging task for project management. Due to the Cone of Uncertainty effect on overall effort estimation and lack of representative effort distribution data, project managers often find it difficult to plan for staffing and other team resources. This often leads to risky decisions to assign too few or too many people to complete software lifecycle activities. As a result, projects with inaccurate resource allocation will generally experience serious schedule delay or cost overrun, which has been the outcome of 44% of the projects reported by the Standish Group [Standish, 2009]. Due to lack of data, most effort estimation models, including COCOMO II, use a one-size-fits-all distribution of effort by phase and activity. The availability of a critical mass of data from U.S. Defense Department software projects on effort distribution has enabled me to test several hypotheses that effort distributions vary by project size, personnel capability, and application domains. This dissertation will summarize the analysis approach, describe the techniques and methodologies used, and report the results. The key results were that size and personnel capability were not significant sources of effort distribution variability, but that analysis of the influence of application domain on effort distribution rejected the null hypothesis that the distributions do not vary by domains, at least for the U.S. Defense Department sector. The results were then used to produce an enhanced version of the COCOMO II model and tool for better estimation of the effort distributions for the data-supported domains.