Presentation | 2022-01-12 Execution-trace embedding using word-proximity metric for a method to automatically classify test results Takuma Ikeda, Kozo Okano, Shinpei Ogata, Shin Nakajima, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(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 this paper, we propose a method for convolution of execution traces in machine learning models that aims to solve the test oracle problem. In the proposed method, we focus on the fact that the execution trace is a sequence of method invocations, and obtain a variance vector for each method invocation information using Word2vec. The variance vector of the execution trace is obtained by giving the obtained variance vector to a Long Short-Term Memory (LSTM) and encoding it. Using the learned NN model, we automatically classify the test execution results by grading them as pass or fail. We confirmed the effectiveness of the proposed method by using the execution traces of tests for various programs. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Test Oracle Problem / Supervised Learning / Execution Trace / Cosine Similarity |
Paper # | MSS2021-46,SS2021-33 |
Date of Issue | 2022-01-04 (MSS, SS) |
Conference Information | |
Committee | SS / MSS |
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Conference Date | 2022/1/11(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Nagasakiken-Kensetsu-Sogo-Kaikan Bldg. |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Mathematical Systems Science and its Applications, Software Science, etc. |
Chair | Takashi Kobayashi(Tokyo Inst. of Tech.) / Atsuo Ozaki(Osaka Inst. of Tech.) |
Vice Chair | Kozo Okano(Shinshu Univ.) / Shingo Yamaguchi(Yamaguchi Univ.) |
Secretary | Kozo Okano(Hiroshima City Univ.) / Shingo Yamaguchi(Tokyo Inst. of Tech.) |
Assistant | Shinpei Ogata(Shinshu Univ.) / Masato Shirai(Shimane Univ.) |
Paper Information | |
Registration To | Technical Committee on Software Science / Technical Committee on Mathematical Systems Science and its Applications |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Execution-trace embedding using word-proximity metric for a method to automatically classify test results |
Sub Title (in English) | |
Keyword(1) | Test Oracle Problem |
Keyword(2) | Supervised Learning |
Keyword(3) | Execution Trace |
Keyword(4) | Cosine Similarity |
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) |
Date | 2022-01-12 |
Paper # | MSS2021-46,SS2021-33 |
Volume (vol) | vol.121 |
Number (no) | MSS-317,SS-318 |
Page | pp.pp.83-88(MSS), pp.83-88(SS), |
#Pages | 6 |
Date of Issue | 2022-01-04 (MSS, SS) |