The Use of Simulation Techniques for Hybrid Software Cost Estimation and Risk Analysis

Abstract Cost estimation is a crucial field for companies developing software or software‐intensive systems. Besides point estimates, effective project management also requires information about cost‐related project risks, for example, a probability distribution of project costs. One possibility to provide such information is the application of Monte Carlo simulation. However, it is not clear whether other simulation techniques exist that are more accurate or efficient when applied in this context. We investigate this question with CoBRA®, 1 a cost estimation method that applies simulation, that is, random sampling, for cost estimation. This chapter presents an empirical study, which evaluates selected sampling techniques employed within the CoBRA® method. One result of this study is that the usage of Latin Hypercube sampling can improve average simulation accuracy by 60% and efficiency by 77%. Moreover, analytical solutions are compared with sampling methods, and related work, limitations of the study, and future research directions are described. In addition, the chapter presents a comprehensive overview and comparison of existing software effort estimation methods.