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Fusion of Contrastive Learning and Heterogeneous Graph for Code Summarization 3

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

引用

3rd International Conference on Electronics and Information Technology, EIT 2024

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

ISBN: (纸本)9798350369151

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. © 2024 IEEE.

关键词： Contrastive Learning

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A weakly supervised back-labeling-based method for named entity recognition in automotive manufacturing: MBIC-NER 3

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

引用

3rd International Conference on Electronics and Information Technology, EIT 2024

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

ISBN: (纸本)9798350369151

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. © 2024 IEEE.

关键词： Semantics

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Multi-label code smell Detection Method based on CodeBERT 3

Multi-label code smell Detection Method based on CodeBERT

引用

3rd International Conference on Electronics and Information Technology, EIT 2024

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

ISBN: (纸本)9798350369151

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. © 2024 IEEE.

关键词： Semantics

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Improvement of Federal Average Algorithm Based on CRITIC Weighting Method 2

Improvement of Federal Average Algorithm Based on CRITIC Wei...

引用

2nd International Conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2023

作者： Yu, Qiantong Wei, Nianqing Mi, Yiyang College of Software Chengdu University of Information Technology Chengdu610225 China Sichuan Province Chengdu University of Information Technology Key Laboratory of Software Automatic Generation and Intelligent Service Chengdu610225 China

ISBN: (纸本)9798350371451

Federated learning is a machine learning technology that can protect data privacy. Its federated average algorithm updates the global model through weights. In view of the influence of subjective factors in the calculation of data weights by analytic hierarchy process (AHP), a federated average algorithm based on CRITIC weighting method is proposed to process multi-source data from the perspective of data diversity and volatility. In the experiment, the CRITIC weighting method is used to calculate the importance of each attribute in the data, and it is used as the model aggregation parameter to calculate the weight of each client data. By applying the federated average algorithm in horizontal federated learning to the data model training fusion, the accuracy of data model establishment is improved. In terms of both theoretical basis and practical research, experiments and analysis on the collected data sets show that the proposed method is executable. The experimental results show that the proposed method improves the accuracy by about 4 % compared with the federal average algorithm, reduces the communication overhead of the federal learning and training process, and improves the overall efficiency. © 2023 IEEE.

关键词： Analytic hierarchy process

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Multi-station low-visibility prediction based on spatio-temporal fusion model

Multi-station low-visibility prediction based on spatio-temp...

引用

2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Xiao, Nan Luo, Fei Cao, Liang College of Software Engineering Chengdu University of Information Technology Sichuan Chengdu China Yunnan R&D Institute of Natural Disaster Chengdu University of Information Technology Yunnan Kunming China Automatic Software Generation and Intelligent Service Key Laboratory of Sichuan Province Sichuan Chengdu China CMA - CUIT Meteorological Software Engineering Joint Research Center Sichuan Chengdu China

ISBN: (纸本)9781510680425

The accurate prediction of low-visibility has important application value to the normal operation of navigation, aviation and other transportation vehicles. However, the current low-visibility prediction task mostly uses time series model to extract temporal features, but neglects the spatial correlation between national observation stations. Additionally, those time series model require that the input data must be limited to the Euclidean space. However, due to the discrete distribution of stations, the input data required for multi-station low-visibility prediction does not have a regular spatial structure. To address those limitation, we proposed a weighted location feature convolution based on graph convolution. The construction of the weighted location feature convolution takes into account the distance and pitch angle, reduces the influence caused by the dispersion of national observation stations, and enables the convolution kernel to be shared in different local meteorological spaces. The convolution as a substitute for matrix multiplication within Sequence to Sequence framework, then cascaded the decoder to deal with the spatio-temporal dependence relationship of different levels, leading spatio-temporal fusion model CPGDC. The experimental results show that the CPGDC model achieves has higher accuracy in predicting low-visibility values across multiple national observation stations. © 2024 SPIE.

关键词： Input output programs

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PSO-CNN-BiLSTM-MQA for Credit Risk Prediction of Listed Enterprises Based on Hierarchical Feature Modeling 7

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

引用

7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Xie, Yumiao Wei, Le 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: (纸本)9798350354973

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. © 2024 IEEE.

关键词： Prediction models

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Knowledge graph question answering system based on code defect 3

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

引用

3rd International Conference on Computer Science and Management Technology, ICCSMT 2022

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

ISBN: (纸本)9798350320404

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. © 2022 IEEE.

关键词： Knowledge graph

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A Semantic Code Search Method Based on Program Conversion 2

A Semantic Code Search Method Based on Program Conversion

引用

2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Tao, Yan Wei, Le Shu, Hongping School of Software Engineering Chengdu University of Information Technology Sichuan Chengdu610225 China Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Chengdu610225 China

Aiming at the problem that code fragments can’t be captured accurately and quickly due to ignoring semantic information and structural information of source code in code search task, A semantic code search method based on program conversion is proposed (Semantic Code Search based on Program Conversion, SCSPC). The SCSPC diversified the data of the acquired code fragments through data enhancement, changed the program through variable renaming, exchanging two independent statements, circular exchange, inserting exception capture and if equivalent replacing switch statement, and trained the CodeBERT model with mixed objective functions (masking language modeling and replacing token detection) to generate sentence vectors with rich semantics for the code fragments and natural languages, and compared the similarity of the vectors to complete the code search. Experimental results show that compared with SWIM, QECK and CODEnn models, the average reciprocal ranking and hit rate (S@10) of SCSPC method are increased by 2% and 0.041 respectively. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Semantics

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A Defect Detection Method Based on Code Defect Knowledge Graph 9

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

引用

9th International Conference on Computer and Communications, ICCC 2023

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

ISBN: (纸本)9798350317251

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. © 2023 IEEE.

关键词： Graphic methods

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Improvement of Federal Average Algorithm Based on CRITIC Weighting Method

Improvement of Federal Average Algorithm Based on CRITIC Wei...

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Artificial Intelligence and intelligent Information Processing (AIIIP), International Conference on

作者： Qiantong Yu Nianqing Wei Yiyang Mi College of software Chengdu University of Information Technology Chengdu China Key Laboratory of Software Automatic Generation and Intelligent Service Sichuan Province Chengdu University of Information Technology Chengdu China

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