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| H. Watanabe, S. Matsumoto, Y. Higo, S. Kusumoto, T. Kurabayashi, K. Hiroyuki, and H. Tanno, "Applying Multi-Objective Genetic Algorithm for Efficient Selection on Program Generation," In Proceedings of the 28th Asia-Pacific Software Engineering Conference, pp. 515-519, December 2021. | |
| ID | 727 |
| 分類 | 国際会議 |
| タグ | algorithm applying efficient generation genetic multi-objective program selection |
| 表題 (title) |
Applying Multi-Objective Genetic Algorithm for Efficient Selection on Program Generation |
| 表題 (英文) |
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| 著者名 (author) |
Hiroto Watanabe,Shinsuke Matsumoto,Yoshiki Higo,Shinji Kusumoto,Toshiyuki Kurabayashi,Kirinuki Hiroyuki,Haruto Tanno |
| 英文著者名 (author) |
Hiroto Watanabe,Shinsuke Matsumoto,Yoshiki Higo,Shinji Kusumoto,Toshiyuki Kurabayashi,Kirinuki Hiroyuki,Haruto Tanno |
| 編者名 (editor) |
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| 編者名 (英文) |
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| キー (key) |
Hiroto Watanabe,Shinsuke Matsumoto,Yoshiki Higo,Shinji Kusumoto,Toshiyuki Kurabayashi,Kirinuki Hiroyuki,Haruto Tanno |
| 書籍・会議録表題 (booktitle) |
Proceedings of the 28th Asia-Pacific Software Engineering Conference |
| 書籍・会議録表題(英文) |
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| 巻数 (volume) |
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| 号数 (number) |
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| ページ範囲 (pages) |
515-519 |
| 組織名 (organization) |
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| 出版元 (publisher) |
IEEE |
| 出版元 (英文) |
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| 出版社住所 (address) |
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| 刊行月 (month) |
12 |
| 出版年 (year) |
2021 |
| 採択率 (acceptance) |
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| URL |
https://doi.org/10.1109/APSEC53868.2021.00060 |
| 付加情報 (note) |
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| 注釈 (annote) |
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| 内容梗概 (abstract) |
Automated program generation (APG) is a concept of automatically making a computer program. Toward this goal, transferring automated program repair (APR) to APG can be considered. APR modifies the buggy input source code to pass all test cases. APG regards empty source code as initially failing all test cases, i.e., containing multiple bugs. Search-based APR repeatedly generates program variants and evaluates them. Many traditional APR systems evaluate the fitness of variants based on the number of passing test cases. However, when source code contains multiple bugs, this fitness function lacks the expressive power of variants. In this paper, we propose the application of a multi-objective genetic algorithm to APR in order to improve efficiency. We also propose a new crossover method that combines two variants with complementary test results, taking advantage of the high expressive power of multi-objective genetic algorithms for evaluation. We tested the effectiveness of the proposed method on competitive programming tasks. The obtained results showed significant differences in the number of successful trials and the required generation time. |
| 論文電子ファイル | APSEC_2021_paper_286.pdf (application/pdf) [一般閲覧可] |
| BiBTeXエントリ |
@inproceedings{id727,
title = {Applying Multi-Objective Genetic Algorithm for Efficient Selection on Program Generation},
author = {Hiroto Watanabe and Shinsuke Matsumoto and Yoshiki Higo and Shinji Kusumoto and Toshiyuki Kurabayashi and Kirinuki Hiroyuki and Haruto Tanno},
booktitle = {Proceedings of the 28th Asia-Pacific Software Engineering Conference},
pages = {515-519},
publisher = {IEEE},
month = {12},
year = {2021},
}
|