Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 2
349-353,393

Changelog topic learning model with attention mechanism and siamese neural network

Zhang Xin
Huang Wenchao
Xiong Yan
School of Computer Science & Technology, University of Science & Technology of China, Hefei 230000, China
DOI: 10.19734/j.issn.1001-3695.2022.07.0350

Abstract

In order to further mine the changelog information, this paper proposed a siamese Bi-LSTM network model based on the attention mechanism to classify the changelog to realize topic annotation and assist in locating the location of code defects. The model proposed a word segmentation tool with security features to realize changelog preprocessing, used Bi-LSTM network to learn changelog contextual semantic information, and solved the problem of overfitting mode existing in changelog itself through siamese neural network and expanded the data set with high quality to improve generalization ability. It carried out serialization training for the changelog composed of multiple sentences, and the influence of sentences was distinguished through the attention mechanism. For some changelogs of the defect repair class, this paper improved the LLVM tool, generated a mapping table to search the log content, and located the location of the defect module in the source code. A large number of experimental results show that the classification effect of the model in this paper has strong generalization ability, and is nearly 10% higher than the general methods in text classification methods in terms of accuracy, F1 value and other indicators, has ideal log classification effect and topic learning effect.

Foundation Support

国家重点研发计划资助项目(2018YFB2100300,2018YFB0803400)
国家自然科学基金资助项目(61972369,62102385)
安徽省自然科学基金资助项目(2108085QF262)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.07.0350
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Algorithm Research & Explore
Pages: 349-353,393
Serial Number: 1001-3695(2023)02-005-0349-05

Publish History

[2022-10-18] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

张鑫, 黄文超, 熊焰. 融合注意力机制与孪生神经网络的更新日志主题学习模型 [J]. 计算机应用研究, 2023, 40 (2): 349-353,393. (Zhang Xin, Huang Wenchao, Xiong Yan. Changelog topic learning model with attention mechanism and siamese neural network [J]. Application Research of Computers, 2023, 40 (2): 349-353,393. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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