Abstract: A statistical approach to estimating software execution times is implemented by preparing a model of a target processing device, correlating the software to be estimated to benchmark programs used in the preparation of the model, and then applying the software to be estimated to the model. The model is developed by determining the actual execution times of the benchmark programs, determining a number of virtual instructions in the benchmark programs and determining a predictor equation that correlates the virtual instructions to the actual execution time. The predictor equation is determined by a linear regression technique that includes a correlation analysis of the virtual instructions, removal of highly correlated instructions, and a combination of stepwise linear regression and multiple linear regression to determine the predictor equation.

Abstract: A statistical approach to estimating software execution times is implemented by preparing a model of a target processing device, correlating the software to be estimated to benchmark programs used in the preparation of the model, and then applying the software to be estimated to the model. The model is developed by determining the actual execution times of the benchmark programs, determining a number of virtual instructions in the benchmark programs and determining a predictor equation that correlates the virtual instructions to the actual execution time. The predictor equation is determined by a linear regression technique that includes a correlation analysis of the virtual instructions, removal of highly correlated instructions, and a combination of stepwise linear regression and multiple linear regression to determine the predictor equation. A 2-sample t-test is utilized to evaluate whether the software to be capable of being estimated by the model developed from the benchmark programs.