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| Presentation | 2019-06-14 15:30 Identification comparison of software fault-prone modules using nonlinear logistic regression models Kazunari Yamanaka, Tadashi Dohi, Hiroyuki Okamura (Hiroshima U.)  R2019-12 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this article, we compare several non-linear logistic regression models used in a fault-prone identification problem in terms of the predictive performance. For the project data measured in actual software development projects, we evaluate the F-score defined by the tradeoff between accuracy and recall, and compare three logistic regression models; linear logistic regression, semi-definite logistic regression and kernel regressions. Especially, using the kernel method yields increase of the dimension in the feature vector. Also, since the computation of inner products can be easily replaced by the computation of positive definite kernel functions, we have an advantage to execute the complex non-linear computation with higher speed. Finally, it is shown that the kernel logistic regression models could identify the fault-prone modules more accurately than the existing logistic regression models. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | software fault prediction / fault-prone module / non-linear logistic regression / identification problem / / kernel method / F-score / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 82, R2019-12, pp. 19-24, June 2019. | |
| Paper # | R2019-12 | |
| Date of Issue | 2019-06-07 (R) | |
| ISSN | Print edition: ISSN 0913-5685 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) | |
| Download PDF | R2019-12 |
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| Conference Information | |
| Committee | R |
| Conference Date | 2019-06-14 - 2019-06-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kikai-Shinko-Kaikan Bldg. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Overall reliability engineering |
| Paper Information | |
| Registration To | R |
| Conference Code | 2019-06-R |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Identification comparison of software fault-prone modules using nonlinear logistic regression models |
| Sub Title (in English) | |
| Keyword(1) | software fault prediction |
| Keyword(2) | fault-prone module |
| Keyword(3) | non-linear logistic regression |
| Keyword(4) | identification problem |
| Keyword(5) | |
| Keyword(6) | kernel method |
| Keyword(7) | F-score |
| Keyword(8) | |
| 1st Author's Name | Kazunari Yamanaka |
| 1st Author's Affiliation | Hiroshima University (Hiroshima U.) |
| 2nd Author's Name | Tadashi Dohi |
| 2nd Author's Affiliation | Hiroshima University (Hiroshima U.) |
| 3rd Author's Name | Hiroyuki Okamura |
| 3rd Author's Affiliation | Hiroshima University (Hiroshima U.) |
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| Speaker | Author-2 |
| Date Time | 2019-06-14 15:30:00 |
| Presentation Time | 25 minutes |
| Registration for | R |
| Paper # | R2019-12 |
| Volume (vol) | vol.119 |
| Number (no) | no.82 |
| Page | pp.19-24 |
| #Pages | 6 |
| Date of Issue | 2019-06-07 (R) |
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