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Consistency analysis of UML models 29

Consistency analysis of UML models

引用

29th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2023

作者： Tang, Guo-Fu Jiang, Jian-Min Wen, Hao Automatic Software Generation and Intelligence Service Key Laboratory of Sichuan Province China Chengdu University of Information Technology Chengdu610103 China Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610103 China University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)1891706578

The inconsistencies of UML models (diagrams) during the software development process may cause errors in documents or programs. Massive consistency rules have been proposed by researchers to detect inconsistencies in existing works. However, the approaches of consistency analysis have not been addressed adequately in literature. In this paper, we propose a new approach for analyzing consistencies between UML models. We formally specify UML models and define the consistency relation between UML models. Based on the consistency definition, we first discuss the composition and decomposition of consistencies, and then explore the equivalence of consistencies. Copyright © 2023 by KSI Research Inc. and Knowledge Systems Institute, USA.

关键词： software design

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Fusion of Contrastive Learning and Heterogeneous Graph for Code Summarization

Fusion of Contrastive Learning and Heterogeneous Graph for C...

引用

International Conference on Electronics and Information Technology (EIT)

作者： Caiting Zhong Le Wei School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Manufacturing Information Industry Collaborative Innovation Center Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

Aiming at the shortcomings of existing code summary generation methods in dealing with the semantic complexity, structural diversity of code and the fusion of search summary and generation summary, a code summary generation method combining contrast learning and heterogeneous graph is proposed. Firstly, a dense vector retrieval framework based on double encoders is adopted to enhance the retrieval accuracy and semantic diversity of large-scale code data. Secondly, the abstract syntax tree is reconstructed into a heterogeneous graph. By introducing various node and edge types, the model's ability to capture complex semantic relationships between different node and edge types is deepened. Finally, the copy gate mechanism is introduced to realize the dynamic fusion of retrieval abstracts and generated model output in semantic level, which guarantees the semantic accuracy and relevance of generated abstracts. Experimental results confirm the effectiveness of this approach in improving the precision and relevance of generated code summaries.

关键词： Java Codes Accuracy Semantics Diversity reception Contrastive learning Syntactics Logic gates Vectors Information technology

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A weakly supervised back-labeling-based method for named entity recognition in automotive manufacturing: MBIC-NER

A weakly supervised back-labeling-based method for named ent...

引用

International Conference on Electronics and Information Technology (EIT)

作者： Heyu Yang Le Wei School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Manufacturing Information Industry Collaborative Innovation Center Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

Aiming at the problems of lack of corpus, lack of entity annotation dataset and fragmented data in named entity recognition in the automobile manufacturing field, a new named entity recognition method is proposed: MBIC-NER. The method firstly constructs an automobile manufacturing annotation dataset by using the idea of weakly supervised back annotation, and generates word vector sequences of the input sentences by using MacBERT pre-training model in order to better understand the context and semantic associations, and improves the accuracy and effect of text processing in the field of automobile manufacturing; Secondly, BiLSTM model and IDCNN model are constructed to perform feature extraction on the input word vector sequences to form a multilevel feature representation; Finally, the character sequences are continued to be decoded through the CRF, and the corresponding label of each character is obtained. The result show that the F1-score of MBIC-NER for named entity recognition in the self-constructed automobile manufacturing domain dataset reaches 87.03%, which is better than the other named entity recognition methods in comparison.

关键词： Accuracy Annotations Named entity recognition Knowledge graphs Feature extraction Vectors Manufacturing Automobiles Automotive engineering Context modeling

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Cloud service selection method based on overall similarity in cloud manufacturing environment

Cloud service selection method based on overall similarity i...

引用

2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Liao, Wenli Wei, Le School of Software Engineering Chengdu University of Information Technology Chengdu China Automatic Software Generation and Intelligence Service Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9780738110837

The physical location of manufacturing services cannot be considered well in cloud service selection. In order to solve this problem, a cloud service selection considering the physical location of the manufacturing service is proposed. First, the cloud manufacturing service is described. Second, the type, functional attributes, physical location and service QoS are considered, and manufacturing service chain is formed according to manufacturing task flow. Finally, thresholds and weights are designed by combining four similarity algorithms to narrow the scope of result set and improve accuracy. The analysis of numerical examples shows, this method can narrow the selection range, and ensure that the selected manufacturing services and manufacturing tasks have a high degree of similarity. © 2021 IEEE.

关键词： Numerical methods

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Multi-label code smell Detection Method based on CodeBERT

Multi-label code smell Detection Method based on CodeBERT

引用

International Conference on Electronics and Information Technology (EIT)

作者： Hongming Bao Le Wei School of Software Engineering Chengdu University of Information Technology Chengdu China School of Software Engineering Chengdu University of Information Technology Sichuan Manufacturing Information Industry Collaborative Innovation Center Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

Code smells usually hint at possible problems or design flaws in the code. Efficient code smell detection methods can significantly improve code quality. However, existing code smell detection methods rely heavily on information such as metrics and indicators, fail to make full use of the syntax and semantics of source code, and have shortcomings in multi-label code smell detection. To this end, this paper proposes a Multi-Label Code Smell Detection Based on CodeBERT (MLCSDCB) based on the pre-trained model CodeBERT. Firstly, the constructed data set is preprocessed and standardized to reduce noise interference, and the multi-label data set is constructed by the label power set method. Then, CodeBERT is used to embed the code and extract its rich syntactic and semantic information. Then, Bi-LSTM was used to fully learn the Code Representation Information [CLS] vector of each layer of CodeBERT to enhance the context understanding ability of the model. Finally, the attention mechanism was used to strengthen the key information according to the importance of each layer [CLS] vector, and the multi-label prediction was performed based on the strengthened feature vector to improve the detection effect of the model. Compared with the existing detection methods based on source code representation, MLCSDCB improves the precision, recall and F1 value by 2.02%, 1.04%and 4.75%respectively.

关键词： Measurement Codes Attention mechanisms Source coding Semantics Noise Syntactics Predictive models Vectors Robustness

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PSO-CNN-BiLSTM-MQA for Credit Risk Prediction of Listed Enterprises Based on Hierarchical Feature Modeling

PSO-CNN-BiLSTM-MQA for Credit Risk Prediction of Listed Ente...

引用

Machine Learning and Natural Language Processing (MLNLP), International Conference on

作者： Yumiao Xie Le Wei School of Software Engineering Chengdu University of Information Technology Chengdu China Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province School of Software Engineering Chengdu University of Information Technology Sichuan Manufacturing Information Industry Collaborative Innovation Center Chengdu China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Aiming at the problems of insufficient feature discrimination, difficulty in parameter adjustment, and lack of time dependence capture ability in the existing credit risk prediction models of listed companies, this paper proposes a PSOCNN-BiLSTM-MQA credit risk prediction method PCBM based on hierarchical feature modeling. Based on the financial data of listed companies in Guotai’an database, the method divides the data into four types of first-level characteristics: debt paying ability, operating ability, profitability and development ability. Each class of first-level features is further subdivided into several second-level features. In the feature extraction stage, a twochannel Convolutional Neural Network (CNN) was used to extract the local features of each class of level-1 features. One channel processed the original data, and the other channel processed the features converted to grayscale images. After being processed by different CNNS, the multi-query attention mechanism (MQA) was used to fuse the features from different channels to enhance the attention to features at different levels. At the same time, the bidirectional long short-term memory network is used to capture the long-term dependencies in the time series data, and the fused features are integrated through the MQA mechanism. In order to further improve the performance of the model, the particle swarm optimization algorithm is used to optimize the model parameters. Experimental results show that compared with the existing prediction models, the PCBM model shows significant improvement in accuracy, precision, recall rate and F1-score, which proves the effectiveness of this method in the credit risk prediction of listed companies.

关键词： Transmission line matrix methods Attention mechanisms Profitability Computational modeling Companies Predictive models Gray-scale Feature extraction Data models Convolutional neural networks

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Knowledge graph question answering system based on code defect

Knowledge graph question answering system based on code defe...

引用

Computer Science and Management Technology (ICCSMT), International Conference on

作者： Yang He Le Wei School of Software Engineering Chengdu University of Information Technology Chengdu China Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Chengdu China

In the current scenario of code defect aided search, the code defect knowledge is not accurate enough due to the singularity and island of code defect knowledge, so that developers and testers can not accurately use the defect knowledge. To solve this problem, this paper proposes a question answering system construction method based on code defect knowledge graph. Firstly, a large number of code defect knowledge is cleaned to obtain the entities and relationships of code defect knowledge, and the corresponding code defect knowledge graph is established. Then the combination of Bert model and Prompt makes the system better identify user intentions. The effectiveness of this method is verified based on the accuracy comparison with the commonly used models in this field. Experiments show that the combination of Bert model and Prompt makes this method more accurate in identifying user intention.

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On Corpus Construction and Joint Modeling Method for Clinical Pulse Feeling and Palpation Information Extraction and Lexical Analysis of Traditional Chinese Medicine 22

On Corpus Construction and Joint Modeling Method for Clinica...

引用

22nd Chinese National Conference on Computational Linguistics, CCL 2023

作者： Wang, Yaqiang Jiang, Wen Jiang, Yongguang Shu, Hongping College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Preclinical Medicine Chengdu University of Traditional Chinese Medicine China

ISBN: (纸本)9781713876229

Pulse feeling and palpation (PFP) are the most distinctive and representative clinical diagnostic methods of traditional Chinese medicine (TCM). They provide important evidence for TCM clinical practitioners to differentiate syndromes. Extracting PFP information and analyzing its lexical features have important clinical value. In this paper, we carried out research on corpus construction and joint modeling method for PFP information extraction (IE) and lexical analysis (LA) of TCM for the first time. Based on more than ten thousand TCM clinical records, we proposed a corpus construction framework, built annotation guidelines and constructed a labeled PFP IE and LA corpus to support multi-task learning. Labeling consistency evaluated by inter-annotator agreement value for the corpora achieve more than 0.94. Moreover, we attempted to jointly model PFP IE and LA tasks of TCM with a same-level-shared multi-task model, and experimental results verified effectiveness of the joint model. © 2023 China National Conference on Computational Linguistics.

关键词： Information retrieval

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An Unstructured Data Representation Enhanced Model for Postoperative Risk Prediction 21

An Unstructured Data Representation Enhanced Model for Posto...

引用

21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Wang, Yaqiang Yang, Xiao Hao, Xuechao Shu, Hongping Chen, Guo Zhu, Tao College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Anesthesiology Sichuan University China

Postoperative risk prediction has a positive effect on clinical resource planning, emergency plan preparation and reducing postoperative risk and mortality. Postoperative risk prediction is mainly based on patient's basic information, laboratory tests, vital signs and other structured data, while the value of unstructured preoperative diagnosis with rich semantic information remains to be verified. Aiming at attempting this problem, an unstructured data representation enhanced postoperative risk prediction model is proposed in this paper. The model utilizes self-attention to fuse structured data with preoperative diagnosis. Through comparing with the commonly used statistical machine learning models and the state-of-the-art deep neural networks, the proposed model has not only better prediction performance, but also better interpretability. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Deep neural networks

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A Defect Detection Method Based on Code Defect Knowledge Graph

A Defect Detection Method Based on Code Defect Knowledge Gra...

引用

International Conference on Computer and Communications (ICCC)

作者： He Yang Wei Le Li Yumin School of Software Engineering Chengdu University of Information Technology Chengdu China Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Sichuan Manufacturing Information Industry Collaborative Innovation Center Chengdu University of Information Technology Chengdu China College of Information Engineering Sichuan TOP IT Vocational Institute Chengdu China

To address the limitations of existing code defect detection methods, which only analyze code defects from a single perspective and produce uninterpretable results, we introduce a new approach known as Defect Detection Method based on code defect knowledge Graph (DDMB). This method involves several steps. First, we clean and organize defect-related knowledge, defining concepts of defect entities, attributes, and relationships to establish a code defect knowledge graph. Next, using abstract syntax trees, we extract various information from the code, including control flow diagrams, control dependency graphs, data dependency graphs, program dependency graphs, and code attribute graphs, to capture relevant details about nodes and edges. Based on the structural and logical information represented by node-edge associations, we employ the Deepwalk algorithm to construct a directed graph of the code and generate random walk sequences. These sequences serve as a foundation for learning and vector representation. Finally, we compare the vector similarities between the generated representations and a candidate code list from the defect knowledge graph, which has undergone initial semantic threshold filtering. By querying the defect labels of the most similar code in the defect code knowledge graph, we determine whether the code under inspection contains defects. Experimental results reveal that DDMB improves defect detection accuracy by 2.02% compared to existing methods. Additionally, it enhances the interpretability and reliability of defect detection while facilitating multi-dimensional analysis of code defects, thus providing better support for practical applications.

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