Software performance prediction at source level
Erh-Wen Hu, Bogong Su, Jian Wang · 2017
Performance prediction is critical in embedded system design for reducing the turnaround time of software. Using simulation to measure the performance of the whole source code is often too slow, particularly after the modification of the source code due to changes in problem specification. In this paper we present a comprehensive method that combines analytical modeling and statistical approach to predicting the performance of application software at source code level. We take samples from EEMBC and SMV benchmarks and gather the static attributes from the source code of those samples as our learning set. To determine the effectiveness of our new approach, we select several functions from PHY Benchmark as our testing set. We then apply multiple linear regression technique enhanced with the inclusion of new approaches by using the popular statistical tool SPSS23 to predict the performance of these functions. Comparing our predicted results with the actual measured values, the outcome is promising as the average relative error is within 20%.