This research uses Data Envelopment Analysis (DEA) to evaluate twenty-six software projects. The models use an internal risk and complexity assessment to measure software development output, in conjunction with customer satisfaction. Three models are included in this study: time-to-market, cost estimation, and cost minimization.While software has facilitated unprecedented growth and technological advancement across essentially all industries and markets, productivity improvement continues to lag behind all other aspects of computing advances. Consequently, more sophisticated analysis techniques are required to effectively measure the software process.This research is unique because very few DEA studies have accounted for risk/complexity in modeling software development, at least to the knowledge of this author. In addition, it uses budget variance as a proxy for measuring cost estimation and cost minimization efficiency. Finally, it is unique because it does not rely upon an explicit measure of size to account for software development output.