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| Presentation | 2021-06-12 15:50 Towards More Accurate Software Bug Prediction: Maximum Likelihood Predictions Daigo Fujimura, Tadashi Dohi, Hiroyuki Okamura (Hiroshima Univ.)  R2021-15 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this note, we consider point prediction methods for the number of software bugs decetced in the future with the bug count data experienced in the past, where the underlying software bug-detection process is described by non-homogeneous Poisson processes. In general, it is known that the probability distribution of the number of software bugs observed in the past is not identical to one of the bug counts expected in the future. Nevertheless, the commonly used technique is to predict the bug counts under a strong assumption that the probability distribution with model parameters estimated from the past observations holds even in the future evolution. Since such a plug-in prediction does not work well to guarantee the higher prediction accuracy, the more accurate prediction of software bug counts is an emerging issue in software reliability engineering. We focus on the maximum likelihood predictions and propose two maximum likelihood predictors to predict the future bug-detection processes. Through a numerical example with an actual software bug count data set, we compare our new prediction methods with the existing plug-in predictor. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | software reliability model / non-homogeneous Poisson process / predictive performance / plug-in prediction / maximum likelihood prediction / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 64, R2021-15, pp. 25-30, June 2021. | |
| Paper # | R2021-15 | |
| Date of Issue | 2021-06-05 (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) | |
| Download PDF | R2021-15 |
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| Conference Information | |
| Committee | R |
| Conference Date | 2021-06-12 - 2021-06-12 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online (Zoom) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Reliability General |
| Paper Information | |
| Registration To | R |
| Conference Code | 2021-06-R |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Towards More Accurate Software Bug Prediction: Maximum Likelihood Predictions |
| Sub Title (in English) | |
| Keyword(1) | software reliability model |
| Keyword(2) | non-homogeneous Poisson process |
| Keyword(3) | predictive performance |
| Keyword(4) | plug-in prediction |
| Keyword(5) | maximum likelihood prediction |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Daigo Fujimura |
| 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 | 2021-06-12 15:50:00 |
| Presentation Time | 25 minutes |
| Registration for | R |
| Paper # | R2021-15 |
| Volume (vol) | vol.121 |
| Number (no) | no.64 |
| Page | pp.25-30 |
| #Pages | 6 |
| Date of Issue | 2021-06-05 (R) |
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