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检索条件"任意字段=Knowledge-Based Software Engineering Conference"

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CCT5: A Code-Change-Oriented Pre-trained Model 2023

CCT5: A Code-Change-Oriented Pre-trained Model

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31st ACM Joint Meeting of the European software engineering conference / Symposium on the Foundations-of-software-engineering (ESEC/FSE)

作者： Lin, Bo Wang, Shangwen Liu, Zhongxin Liu, Yepang Xia, Xin Mao, Xiaoguang Natl Univ Def Technol Coll Comp Sci Changsha Peoples R China Zhejiang Univ Hangzhou Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Key Lab Software Engn Complex Syst Changsha Peoples R China

ISBN: (纸本)9798400703270

software is constantly changing, requiring developers to perform several derived tasks in a timely manner, such as writing a description for the intention of the code change, or identifying the defect-prone code changes. Considering that the cost of dealing with these tasks can account for a large proportion (typically around 70 percent) of the total development expenditure, automating such processes will significantly lighten the burdens of developers. To achieve such a target, existing approaches mainly rely on training deep learning models from scratch or fine-tuning existing pre-trained models on such tasks, both of which have weaknesses. Specifically, the former uses comparatively small-scale labelled data for training, making it difficult to learn and exploit the domain knowledge of programming language hidden in the large-amount unlabelled code in the wild;the latter is hard to fully leverage the learned knowledge of the pre-trained model, as existing pre-trained models are designed to encode a single code snippet rather than a code change (i.e., the difference between two code snippets). We propose to pre-train a model specially designed for code changes to better support developers in software maintenance. To this end, we first collect a large-scale dataset containing 1.5M+ pairwise data of code changes and commit messages. based on these data, we curate five different tasks for pre-training, which equip the model with diverse domain knowledge about code changes. We fine-tune the pre-trained model, CCT5, on three widely-studied tasks incurred by code changes and two tasks specific to the code review process. Results show that CCT5 outperforms both conventional deep learning approaches and existing pre-trained models on these tasks.

关键词： Code Change Pre-Training Deep Learning

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Improving temporal question answering using temporal knowledge graph embedding 4

Improving temporal question answering using temporal knowled...

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4th International conference on Computer engineering and Application, ICCEA 2023

作者： Chen, Jiabao Fan, Yongquan School of Computer & Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798350347548

knowledge graph question answering is an important research direction for question answering tasks. In recent years, there has been an increasing amount of research on knowledge graph question answering, but temporal knowledge graph question answering is still a relatively unexplored area. In question answering tasks, many natural language questions have explicit or implicit temporal constraints, and most existing research methods lack the temporal awareness to deal with complex temporal questions. To address these challenges, this paper proposes a question answering model based on temporal knowledge graph embedding (TKGETQA). The model uses TKG embeddings to root a question in the entities, relations and time horizons it references, and perceives temporal information from the question to improve the accuracy of answer prediction. Experiments on the dataset CronQuestions in this paper show that the TKGETQA model exhibits better results compared to existing temporal knowledge graph question answering approaches. © 2023 IEEE.

关键词： knowledge graph

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An Efficient Lossless Graph Summarization Method for Large Streaming Graphs 35

An Efficient Lossless Graph Summarization Method for Large S...

引用

35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Wang, Qia Wang, Yi Wang, Ying College of Computer and Information Science College of Software Southwest University ChongQing China

Graph summarization aims to extract critical information from large graphs by creating summaries that represent the original data. Especially, real-world daily applications generate massive dynamic streaming graphs, representing as edge or node streams. How to efficiently generate compact and lossless graph summaries for large streaming graphs is still a challenging problem. This paper proposes an efficient and scalable lossless summarization method for streaming graphs, called Partition and Similarity-based Two-Stage Summarization (PSTSS). PSTSS uses a streaming graph partitioning algorithm as the first stage to generate coarse-grained summaries by on-the-fly subgraph partitioning. The second stage generates fine-grained supernodes within these coarse-grained supernodes through efficient node similarity calculation. Our experiments on six large datasets demonstrate that our method achieves better operational efficiency, compression rate, scalability, and graph query speed compared to state-of-the-art lossless summarization algorithms. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Large dataset

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Detection of Broken Access Control Vulnerability Using Graph Representation

Detection of Broken Access Control Vulnerability Using Graph...

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2024 IEEE International conference on Data and software engineering, ICoDSE 2024

作者： Dharmasatya, Fawwaz Anugrah Wiradhika Asnar, Yudistira School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798331506407

Broken access control is still one of the most common vulnerabilities. This vulnerability can cause unauthorized information disclosure, modification, or destruction of all data or performing business function outside the user’s limits. One method to detect broken access control is static code analysis. Various tools use different static code analysis techniques, mostly graph-based, such as finding vulnerability pattern using graph analysis, calculating graph similarity, graph embedding, and deep learning. Some of graphs that are commonly used are abstract syntax tree and control flow graph. However, there are several problems in existing tools such as framework-dependent and knowledge base that still depends on tool maker. This paper is aimed to develop static code analysis tool for Python code that utilize finding vulnerability pattern using graph analysis method. This tool allows users to add their own knowledge base, which is an advantage compared to existing tool. From the testing results on 16 projects, the tool has a recall value of 100% and a precision value of 63.24%. The recall value that reaches 100% and the significant number of false positives occurred because the tool assumes that all detected code has a broken access control vulnerability by default until access control check is found. © 2024 IEEE.

关键词： Access control

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A Zero-knowledge Set Membership Proof Scheme based on the SM2 Algorithm 24

A Zero-Knowledge Set Membership Proof Scheme Based on the SM...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Zhou, Yin Dai, Bingrong Li, Chao School of Computer and Information Engineering Shanghai Polytechnic University My Street Shanghai201209 China Department of Computing Shanghai Development Center of Computer SoftWare Technology Shanghai201102 China

ISBN: (纸本)9798400717147

With the widespread application of blockchain technology, various range proof protocols based on zero-knowledge proofs have been proposed. However, existing range proof protocols suffer from issues such as high communication overhead and computational complexity. Therefore, this paper introduces an efficient and secure Zero-knowledge Set Membership Proof Protocol (ZSMPP) to address these challenges. Building upon improvements to the proof structure of range proof protocols, the paper integrates the SM2 identity-based digital signature algorithm, effectively avoiding the time-consuming bilinear pairing operations and reducing computational costs. The proposed protocol offers an efficient and secure solution for the given problem. Experimental results demonstrate that, compared to protocols proposed by Bootle, Deng, Mao, and others, the protocol presented in this paper exhibits superior computational efficiency, providing an efficient and secure solution for data security and individual privacy protection in the digital age. © 2024 ACM.

关键词： Authentication

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Development of LLM-based System for IT Talent Interview

Development of LLM-Based System for IT Talent Interview

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2024 IEEE International conference on Data and software engineering, ICoDSE 2024

作者： Pasaribu, David K.H. Dewandaru, Agung Saptawati, G.A. Putri School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798331506407

The recruitment process usually requires a lot of resources and time. In the IT field, recruiters must also have domain knowledge in the IT field to be able to recruit well. Technological developments have given rise to various technologies that can help recruiters in the recruitment process. In the proposed approach, interviews are carried out by conducting interviews for each competency point given by the recruiter. Interviews consist of 2 stages, namely behavioral and technical. Behavioral interviews were conducted using BEI (Behavioral Event Interview) using the STAR (Situation, Task, Action, and Result) method. Behavioral and technical interview assessments are carried out using a 2-class classification. The results of the classification are aggregated to get a score for the whole interview. Few-shot learning was carried out on GPT-4o to generate interview questions. Evaluation with G-Eval on STAR question generation showed answerability metric score of 0.8938, listening metric score of 0.9633, relevance metric score of 0.986, and STAR completeness metric score of 0.9532. Interview evaluation or scoring was carried out by fine-tuning the Longformer model for behavioral interview assessment and few-shot learning on LLM GPT-4 for technical interview assessment. The use of RAG using GLiNER on a knowledge base containing knowledge about terms in the IT field is carried out to help augment the output of Longformer. The knowledge base used is Wikipedia. The data used in the fine-tuning process is synthetic data created with prompt engineering on GPT-4 and GPT-4o. The results of fine-tuning the Longformer model to classify behavioral interview results have an F1-score of 0.962, an accuracy value of 0.967, a precision value of 0.970, and a recall value of 0.967. © 2024 IEEE.

关键词： Classification (of information)

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Oil Well Acoustic Dynamic Liquid Level Detection based on EEMD 3

Oil Well Acoustic Dynamic Liquid Level Detection Based on EE...

引用

3rd International conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Yang, Ming Sha, Zhiqiang Sun, Yanqing School of Information Engineering Harbin University Harbin150086 China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin University Harbin150086 China Daqing Software Technology Research Institute Co. Ltd. Daqing163711 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection of oil well dynamic liquid level using acoustic methods requires digital signal processing techniques to reduce environmental noises. Most denoising techniques consist of filter-based approaches and spectral methods with time or frequency transformations. In this study, the Ensemble Empirical Mode Decomposition (EEMD) algorithm is implemented at the filtering step within the standard procedures for oil industrial use to detect dynamic liquid level. Five datasets from oil well production are tested using EEMD compared to standard filtering procedures in industrial use. The EEMD algorithm achieves results in good agreement with the reference results in general with worst case relative error reaching 0.51%. In particular, EEMD processed signal visibly displays more salient echo wave feature compared to reference for one case with unknown submerging noises or disturbances. Under such circumstances, the combination of a denoising filter and EEMD furthermore stabilizes the results, the maximum relative error falling to 0.39%. Importantly, the ensemble averaged Intrinsic Mode Functions (IMFs) of frequencies linked to reflected infra-sound could provide good knowledge helping locate the reflection of infra-sound wave. EEMD is a promising method for filtering signals of echo wave based dynamic liquid level detection. Further in-depth investigations are required to better interpret signals mixed with unknown noises or disturbances. © 2024 SPIE.

关键词： Empirical mode decomposition

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Sybil attack simulation on Enhanced IdAPoS protocol with different network topology 12

Sybil attack simulation on Enhanced IdAPoS protocol with dif...

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12th IEEE International conference on Identification, Information and knowledge in the Internet of Things, IIKI 2024

作者： Chen, Shengyu Jin, Shenghao Wei, Yigang Zhang, Hui Beihang University State Key Laboratory of Complex & Critical Software Environment School of Computer Science & Engineering Beijing China Beihang University School of economics and management Beijing China

ISBN: (纸本)9798331510633

This study mainly focus on Sybil attacks with the Identity-Augmented Proof-of-Stake (IdAPoS) protocol under different network topologies, including random, scale-free, and hierarchical networks. The study finds that scale-free networks are more resistant to Sybil attacks, delaying their effects. Furthermore, the research improves the IdAPoS protocol by introducing active strategies for honest nodes, which improves the behaviours of this protocol, and by enabling them to dynamically assess and update their trusted and suspicious authority lists based on on-ledger data, which improves the completeness of this protocol. © 2024 IEEE.

关键词： Network topology

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Slack Use in Large-Scale Agile Organizations: ESN Tools as Catalysts for Alignment? 25th

Slack Use in Large-Scale Agile Organizations: ESN Tools as C...

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25th International conference on Agile software Development (XP)

作者： Stray, Viktoria Barbala, Astri SINTEF Digital N-7034 Trondheim Norway Univ Oslo N-0373 Oslo Norway

ISBN: (纸本)9783031611537;9783031611544

This paper examines the role of the enterprise social networking (ESN) tool Slack in the daily work of software practitioners within NAV, a large-scale agile public sector organization. based on 13 interviews with NAV developers, our case study explores how Slack is employed for knowledge sharing and daily communication across the organization. We used a newly developed framework for communication in agile teams as a theoretical lens. Through our analysis, we found that Slack use had become deeply integrated into the organizational culture and fostered alignment in three main ways: Promoting communication transparency through open discussions visible for developers organization-wide, enhancing communication quality with prompt responses and constant communication, and encouraging communication discipline through structured channels and threads. This study also unveiled some challenges, such as information overload and hindered focus. However, our findings suggest that if common hurdles are overcome, modern ESN tools can reshape how cross-organizational communication plays out in large-scale agile, reinforcing the agile principles of collaboration and motivated individuals.

关键词： Collaboration Tools Team Communication Platforms Agile software Development Human and Social Aspects of software engineering Online Collaborative software Instant Messaging

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DPSPC: A Density Peak-based Statistical Parallel Clustering Algorithm for Big Data 17th

DPSPC: A Density Peak-Based Statistical Parallel Clustering ...

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17th International conference on knowledge Science, engineering and Management (KSEM)

作者： Pan, Xiaohui Deng, Jinglan Yang, Hanyu Peng, Jing Yin, Jianfei Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9789819754946;9789819754953

Data parallelism is a powerful design paradigm for clustering tasks involving large datasets. However, existing solutions suffer from three problems: (i) using non-identical distribution based partitioning methods may pose the risk of data skew;(ii) frequent communication among the divided data partitions may lead to potential performance degradation;and (iii) unnecessary full computation results in significant computational overhead. In order to address these issues, we propose a density peak-based statistical parallel clustering algorithm for big data (DPSPC). Our sampling-based approach creates equal-sized data blocks with the same statistical measures of clusters, reducing data skew and eliminating inter-block communication. By sampling a subset of blocks for computation, we avoid full computation. The experimental results suggest that the NMI index of the DPSPC algorithm is generally not 10% lower than that of other distributed density peak clustering algorithms, with runtime about one-tenth and the lowest communication volume.

关键词： Statistical Parallel Distributed Clustering Density Peak Random Sample Partition Approximate Computation

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