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Intelligent Prediction of Execution Times
Citation key tetzlaff13ICIA
Author Dirk Tetzlaff and Sabine Glesner
Pages 234–239
Year 2013
ISBN 978-1-4673-5255-0
DOI 10.1109/ICoIA.2013.6650262
Journal Proceedings of the Second International Conference on Informatics & Applications (ICIA2013)
Publisher IEEE Computer Society
Abstract It is a major challenge in software engineering to statically analyze in advance the expectable run-time behavior of applications. The most needed information is the expected execution time of a function to determine its computational cost. In this paper, we present a sophisticated approach that solves this problem by utilizing Machine Learning (ML ) techniques based on regression modeling to automatically derive precise predictions for this information. This enables to focus optimization efforts on the parts that are relevant for the resulting performance and to predict execution times of functions on different processing elements of a heterogeneous architecture. Among others, our approach eliminates the need for manual annotations of run-time information, which automates and facilitates the development of complex software, thus improving the software engineering process. For our experiments that demonstrate the accuracy of our approach, we have used a considerable number of programs from various benchmark suites which encompass different real-world application domains. This shows on the one hand the general applicability and on the other hand the high scalability of our ML techniques.
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