Performance Prediction of Embedded System at Source Level

Developers need performance prediction tools that are capable of providing information on the future performance of the embedded system. This paper describes a performance analyser tool developed to predict the performance. We have implement to simpler, realistic and implementable analytical models based on the sound principles of Performance Engineering and Regression Techniques. System design is for reducing the turnaround time of software development. Also reducing the turnaround time after the modification of the source code due to changes in problem specification. Predicting the performance of application software at source code level using comprehensive method that combines analytical modeling and statistical approach. We take samples from EEMBC and SMV benchmarks and gather the static attributes from the source code of those samples as our learning set. We then apply multiple linear regression technique enhanced with statistical tool SPSS23 to predict the performance of these functions.