Volume 14 Number 6 (Jun. 2019)
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JSW 2019 Vol.14(6): 247-256 ISSN: 1796-217X
doi: 10.17706/jsw.14.6.247-256

Comparing DSP Software Performance Prediction Models at Source Code Level — From Analytical to Statistical

Erh-Wen Hu1, Weihua Liu1, Bogong Su1*, Jian Wang2
1Dept. of Computer Science, William Paterson University, Wayne, NJ, USA
2Mobile Broadband Software Design, Ericsson, Ottawa, ON, Canada

Abstract—Efficient performance prediction at source code level is essential in reducing the turnaround time of software development, particularly when the source code is subject to changes due to modification of problem specification. In this paper, we investigate and compare five performance prediction models from practical standpoint to determine the usefulness of these models. To verify the effectiveness of these models, we select a set of functions from PHY DSP Benchmark and TIC64 DSP processor for experiment. Comparing the predicted performance to the actual measured execution time, we observed that the relative prediction error generated from two of the five models are low and can thus be used for practical purposes.

Index Terms—Performance prediction, source code level, analytical model, statistic model. 


Cite:Erh-Wen Hu, Weihua Liu, Bogong Su, Jian Wang, "Comparing DSP Software Performance Prediction Models at Source Code Level — From Analytical to Statistical," Journal of Software vol. 14, no. 6, pp. 247-256, 2019.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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