Presentation 2023-03-14
Toward improving code execution speed using micro benchmark dataset
Fuki Omori, Akinori Ihara, Kazuya Saiki, Yutaro Kashiwa,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) There are some implementation approach with same functionality, but their execution speeds are different. It is difficult to manually examine implementation methods for each function of a large software. In this study, we build an automatic program modification model specialized for improving execution speed using a neural machine translation model with evaluation results collected from MeasureThat.net, a microbenchmark sharing service, as a training data set.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Execution speed / Automatic program repair / micro-benchmarks / neural machine translation / JavaScript
Paper # SS2022-56
Date of Issue 2023-03-07 (SS)

Conference Information
Committee SS
Conference Date 2023/3/14(2days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Kozo Okano(Shinshu Univ.)
Vice Chair Yoshiki Higo(Osaka Univ.)
Secretary Yoshiki Higo(Shinshu Univ.)
Assistant Shinsuke Matsumoto(Osaka Univ.)

Paper Information
Registration To Technical Committee on Software Science
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Toward improving code execution speed using micro benchmark dataset
Sub Title (in English)
Keyword(1) Execution speed
Keyword(2) Automatic program repair
Keyword(3) micro-benchmarks
Keyword(4) neural machine translation
Keyword(5) JavaScript
1st Author's Name Fuki Omori
1st Author's Affiliation Wakayama University(Wakayama Univ.)
2nd Author's Name Akinori Ihara
2nd Author's Affiliation Wakayama University(Wakayama Univ.)
3rd Author's Name Kazuya Saiki
3rd Author's Affiliation Wakayama University(Wakayama Univ.)
4th Author's Name Yutaro Kashiwa
4th Author's Affiliation Nara Institute of Science and Technology(NAIST)
Date 2023-03-14
Paper # SS2022-56
Volume (vol) vol.122
Number (no) SS-432
Page pp.pp.55-60(SS),
#Pages 6
Date of Issue 2023-03-07 (SS)