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| Presentation | 2022-03-07 11:20 Trace Ablation and Fault Localization per Method Using Machine Learning Models for Automatic Classification of Test Execution Results Takuma Ikeda, Kozo Okano, Shinpei Ogata (Shinshu Univ.), Shin Nakajima (NII)  SS2021-44 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The problem to solve automatically classifying the results of test executions is called the test oracle problem. This is one of the most important problems in test automation. In the existing method, we focus on the fact that the execution trace is a sequence of method calls. Our existing method uses Word2vec to obtain the variance vector for each method call information and train the NN model. By performing ablation on a part of the execution trace handled by the existing method, we investigate the information required for the execution trace and the accuracy of the machine learning model. In addition, by deleting the function call information, we estimate the fault location for each method based on the accuracy of the NN model when the fault function information is deleted. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Test Oracle Problem / Supervised Learning / Execution Trace / Ablation / Fault-Localization / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 416, SS2021-44, pp. 13-18, March 2022. | |
| Paper # | SS2021-44 | |
| Date of Issue | 2022-02-28 (SS) | |
| 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) | |
| Notes on Review | This article is a technical report without peer review, and its polished version will be published elsewhere. | |
| Download PDF | SS2021-44 |
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| Conference Information | |
| Committee | SS |
| Conference Date | 2022-03-07 - 2022-03-08 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Software Science etc. |
| Paper Information | |
| Registration To | SS |
| Conference Code | 2022-03-SS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Trace Ablation and Fault Localization per Method Using Machine Learning Models for Automatic Classification of Test Execution Results |
| Sub Title (in English) | |
| Keyword(1) | Test Oracle Problem |
| Keyword(2) | Supervised Learning |
| Keyword(3) | Execution Trace |
| Keyword(4) | Ablation |
| Keyword(5) | Fault-Localization |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Takuma Ikeda |
| 1st Author's Affiliation | Shinshu University (Shinshu Univ.) |
| 2nd Author's Name | Kozo Okano |
| 2nd Author's Affiliation | Shinshu University (Shinshu Univ.) |
| 3rd Author's Name | Shinpei Ogata |
| 3rd Author's Affiliation | Shinshu University (Shinshu Univ.) |
| 4th Author's Name | Shin Nakajima |
| 4th Author's Affiliation | National Institute of Informatics (NII) |
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| Speaker | Author-1 |
| Date Time | 2022-03-07 11:20:00 |
| Presentation Time | 25 minutes |
| Registration for | SS |
| Paper # | SS2021-44 |
| Volume (vol) | vol.121 |
| Number (no) | no.416 |
| Page | pp.13-18 |
| #Pages | 6 |
| Date of Issue | 2022-02-28 (SS) |
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