Learning Component Size Distributions for Software Cost Estimation: Models Based on Arithmetic and Shifted Geometric Means Rules

Understanding software size distribution is critical to cost estimation using COCOMO model and design of reliable production function model. This paper proposes validates a theoretical framework based on the maximization Shannon entropy learn component systems when partial information about moments given. Specification appropriate moment constraints either in form shifted geometric mean or arithmetic both means are considered. The models validated 30 real datasets. analysis reveals that where sizes depict power-law behavior governed by whereas those which shows exponential described mean. Another type system also considered found gamma distribution. Such characterized specification means. study underlines use modern object-oriented programming languages adheres indicating existence team synergies leading substantial containment costs compared traditional procedural languages.