Paper Abstract and Keywords |
Presentation |
2021-07-17 14:25
Refined Ensemble Learning Algorithms for Software Bug Prediction
-- Metaheuristic Approach -- Keisuke Fukuda, Tadashi Dohi, Hiroyuki Okamura (Hiroshima Univ.) R2021-19 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we propose to apply three metaheuristic algorithms; latin hypercube sampling, ABC (artificial
bee colony) algorithm, and parameter free GA (genetic algorithm), to search the optimal hyperparameters in the random forest,
and investigate the prediction accuracy of bug-prone modules. In experiments with actual software development project data,
we compare our refined ensemble learning algorithms with the existing machine learning approaches. It is shown that the random
forests with metaheuristic refinement could provide the better predictive performances of the software bug prediction in average. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
software bug prediction / bug-prone module / random forest / ensemble method / machine learning / hyperparameter / metaheuristics / search-based software engineering |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 115, R2021-19, pp. 18-23, July 2021. |
Paper # |
R2021-19 |
Date of Issue |
2021-07-10 (R) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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R2021-19 |
Conference Information |
Committee |
R |
Conference Date |
2021-07-17 - 2021-07-17 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Virtual |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Reliability Theory, Communication Network Reliability, Reliability General |
Paper Information |
Registration To |
R |
Conference Code |
2021-07-R |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Refined Ensemble Learning Algorithms for Software Bug Prediction |
Sub Title (in English) |
Metaheuristic Approach |
Keyword(1) |
software bug prediction |
Keyword(2) |
bug-prone module |
Keyword(3) |
random forest |
Keyword(4) |
ensemble method |
Keyword(5) |
machine learning |
Keyword(6) |
hyperparameter |
Keyword(7) |
metaheuristics |
Keyword(8) |
search-based software engineering |
1st Author's Name |
Keisuke Fukuda |
1st Author's Affiliation |
Hiroshima University (Hiroshima Univ.) |
2nd Author's Name |
Tadashi Dohi |
2nd Author's Affiliation |
Hiroshima University (Hiroshima Univ.) |
3rd Author's Name |
Hiroyuki Okamura |
3rd Author's Affiliation |
Hiroshima University (Hiroshima Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-07-17 14:25:00 |
Presentation Time |
25 minutes |
Registration for |
R |
Paper # |
R2021-19 |
Volume (vol) |
vol.121 |
Number (no) |
no.115 |
Page |
pp.18-23 |
#Pages |
6 |
Date of Issue |
2021-07-10 (R) |
|