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

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LLM-based Digital Twin Water Conservancy knowledge Graph Construction 36

LLM-Based Digital Twin Water Conservancy Knowledge Graph Con...

引用

36th IEEE International conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Yang, Yan Ye, Feng Xu, Dong Zhang, Xuejie Xu, Jin College of Computer Science and Software Engineering Hohai University Nanjing China College of Water Conservancy and Hydropower Engineering Hohai University Nanjing China

ISBN: (纸本)9798331527235

In the study, a novel method for extracting domain-specific knowledge in digital twin water conservancy construction is proposed, addressing the challenges of interdisciplinary complexity and the limitations in existing knowledge extraction models' understanding capabilities of domain-specific knowledge. Utilizing a large language model, the novel method incorporates the domain knowledge of digital twin water conservancy into a local model framework. It employs prompt-based fine-tuning and leverages the semantic understanding and generative capabilities of the model to enhance knowledge extraction precision. To optimize entity extraction, innovative heterogeneous entity alignment strategies are introduced. The efficacy of the proposed method is validated through comparative and ablation studies conducted on a water domain corpus. Results indicate a significant improvement over conventional models, with F1 scores for entity and relationship extraction at 88.63% and 84.46%, respectively, and an entity extraction precision rate of 90.11%. Ablation experiments further demonstrate that our method outperforms the baseline large language model, enhancing the F1 scores for entity and relation extraction by 5.5 and 3.2 percentage points, respectively. By adjusting the amount of domain knowledge injected, the influence of the amount of domain text on the knowledge extraction effect is explored. © 2024 IEEE.

关键词： knowledge graph

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Smart Contract Vulnerability Detection based on Mixed Channel Attention 36

Smart Contract Vulnerability Detection Based on Mixed Channe...

引用

36th International conference on software engineering and knowledge engineering, SEKE 2024

作者： Wang, Jiao Ran Zeng, Cheng Ba, Kai Yang Quan, Qing Yu Wang, Yi Feng School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China School of Cyberspace Security Hubei University Wuhan China Key Laboratory of Intelligence Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

As blockchain technology progresses, the deployment of smart contracts has become increasingly prevalent. Despite their growing popularity, smart contracts introduce substantial security risks and vulnerabilities. Present detection methodologies predominantly analyze the entirety of a contract’s source code, a process that tends to indiscriminately extract uniform features, leading to inadequate detection capabilities. Addressing these concerns, we propose a novel vulnerability detection method for smart contracts, predicated on a mixed channel attention mechanism. This approach focuses on extracting multifaceted features from pivotal code segments of smart contracts, utilizing a combination of spatial and frequency domain channel attention mechanisms. These mechanisms dynamically modulate the weights of feature channels during visualization, allowing the model to allocate greater emphasis to features of higher significance and enhance feature differentiation. This strategy is instrumental in capturing salient information within the data, thereby augmenting the model’s performance and accuracy. Empirical results substantiate that our proposed method substantially surpasses the baseline model, evidencing improvements of 5.55%, 5.42%, and 5.49% in the detection of timestamp vulnerabilities, and 4.12%, 4.29%, and 4.20% in the identification of reentrancy vulnerabilities, respectively. Our method offers a significant advancement in the efficacy of smart contract vulnerability detection and presents valuable implications for future research. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： channel attention key code slicing multiple features smart contracts vulnerability detection

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Vulnerability Detection based on Adapter Tuning and Enhanced Feature Learning 36

Vulnerability Detection Based on Adapter Tuning and Enhanced...

引用

36th International conference on software engineering and knowledge engineering, SEKE 2024

作者： Luo, Hui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China PengCheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China

ISBN: (纸本)1891706594

Pre-trained code models have achieved promising results in the vulnerability detection field. The prevailing approach is to adapt these models with vulnerability datasets using inefficient full-model fine-tuning. These pre-trained models primarily capture the semantic features of code, while neglecting its structural characteristics. To address these issues, this paper proposes a vulnerability detection method with adapter tuning and enhanced feature learning. First, adapter modules are introduced to UniXcoder and tune only the parameters in the adapters to extract semantic features. This significantly reduces the number of training parameters and better adapts the model to downstream tasks. Then, structural features, including control and data flow information, are extracted from the Program Dependence Graph (PDG) to compensate for the limitations of pre-trained models that rely solely on semantic features. Finally, the semantic and structural features are integrated to train the detection model. The experimental results demonstrate that our proposed method outperforms state-of-the-art approaches and is highly efficient in terms of both training parameters and training data. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Data flow graphs

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Recovering Trace Links Between software Documentation And Code 24

Recovering Trace Links Between Software Documentation And Co...

引用

44th ACM/IEEE International conference on software engineering, ICSE 2024

作者： Keim, Jan Corallo, Sophie Fuchss, Dominik Hey, Tobias Telge, Tobias Koziolek, Anne Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798400702174

Introduction software development involves creating various arti-facts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions. Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using component-based architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code. Results We evaluate our approaches with five open-source projects, comparing our results to baseline approaches. The model-to-code TLR approach achieves an average F_1 -score of 0.98, while the documentation-to-code TLR approach achieves a promising average F_1-score of 0.82, significantly outperforming baselines. Conclusion Combining two specialized approaches with an interme-diate artifact shows promise for bridging the semantic gap. In future research, we will explore further possibilities for such transitive approaches. © 2024 ACM.

关键词： software design

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Structural Elements of knowledge-Oriented Information Exchange in the Universities 18

Structural Elements of Knowledge-Oriented Information Exchan...

引用

18th IEEE International conference on Computer Science and Information Technologies, CSIT 2023

作者： Romanyshyn, Yulia Sheketa, Vasyl Pikh, Volodymyr Pikh, Maria Vovk, Roman Petryshyn, Roman National Tech. University of Oil&Gas Institute of Information Technologies Ivano-Frankivsk Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Software Engineering Ukraine

ISBN: (纸本)9798350360462

There are considered theoretical approaches for the implementation of a web-based learning environment of higher education institutions as a type of online-based learning environment and network surroundings, which provides registered participants of virtual learning communities with access to the functions of knowledge-oriented information exchange using the latest social software communication tools. The central element of the study was obtaining new results regarding to the web-based learning environment and the relevance of the contexts of its functioning. Approaches based on virtual communities and knowledge networks are singled out as basic theoretical approaches. The role of virtual learning communities is substantiated and their main constructive elements for implementation in the web-based learning environment of the university were given. It was determined that virtual communications are key elements in the processes of data and knowledge exchange in universities. In the study, learning communication is understood as the processes of data and knowledge exchange between members of the learning community within learning platforms and university learning environments. © 2023 IEEE.

关键词： E-learning

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RSChat:Intelligent Question Answering Model for Railway Safety knowledge 24

RSChat:Intelligent Question Answering Model for Railway Safe...

引用

24th IEEE/ACIS International conference on Computer and Information Science, ICIS 2024

作者： Li, Jinyu Li, Chao Niu, Sen Dai, Bingrong School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350376647

The release of ChatGPT heralds a new era in artificial intelligence, as large language models (LLMs) empower myriad industries. This paper introduces RSChat, a model based on supervised fine-tuning of multiple large language models (LLMs), exploring a novel paradigm for applying knowledge in railway safety. Initially, we perform prompt engineering using collected domain data, including key information extraction and knowledge expansion. Subsequently, leveraging the constructed fine-tuning datasets, we conduct supervised fine-tuning using multiple large language models (LLMs) as base models. Finally, experimental evaluations demonstrate that RSChat can generate more professional responses in the railway safety domain, exhibiting robust potential for application. © 2024 IEEE.

关键词： Railroad accidents

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EvalQuiz — LLM-based Automated Generation of Self-Assessment Quizzes in software engineering Education

EvalQuiz — LLM-based Automated Generation of Self-Assessmen...

引用

Ein herzliches Willkommen zum Tagungsband der Tagung "software engineering im Unterricht der Hochschulen", SEUH 2024 - 2024 conference "software engineering in Higher Education", SEUH 2024

作者： Meißner, Niklas Speth, Sandro Kieslinger, Julian Becker, Steffen University of Stuttgart Institute of Software Engineering Germany

ISBN: (纸本)9783885797401

Self-assessment quizzes after lectures, educational videos, or chapters are a commonly used method in software engineering (SE) education to allow students to test the knowledge they have gained. However, creating these quizzes is time-consuming, cognitively exhausting, and complex, as an expert in the field needs to create the quizzes and review the lecture material for validity. Therefore, this paper presents a concept to automatically generate self-assessment quizzes based on lecture material using a large language model (LLM) to reduce lecturers’ workload and simplify the general quiz creation process. The developed prototype was handed to experts, who subsequently evaluated the approach. The results show that automatic quiz generation saves time, and the quizzes cover the delivered lecture material well. However, the generated quizzes often lack originality and versatility. Therefore, further prompt engineering might be required to achieve more elaborate results. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： software testing

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A Hybrid Approach to Extraction of knowledge From Scientific Texts based on Large Language Models and Domain Dictionaries

A Hybrid Approach to Extraction of Knowledge From Scientific...

引用

2024 IEEE International Multi-conference on engineering, Computer and Information Sciences, SIBIRCON 2024

作者： Chusova, Alina Artemieva, Irina Chusov, Andrey Far Eastern Federal University Department Software Engineering and Artificial Intelligence Vladivostok Russia

ISBN: (纸本)9798331532024

The vast information landscape of the Internet constitute a significant challenge for extracting valuable content. The lack of standardized data models and structures necessitates ad hoc solutions, often requiring expert knowledge that developers may lack. While Large Language Models (LLMs) hold promise for addressing this challenge, their susceptibility to AI hallucinations and inaccuracies necessitates ongoing research. This paper introduces a hybrid approach for extracting information on computational methods that combines domain-specific dictionaries with LLMs in order to improve the accuracy of method categorization. Our system incorporates explainability, allowing users to understand the reasoning behind method assignments. Furthermore, user-driven training is facilitated by allowing users to select theories and highlight relevant keywords, enhancing learning capabilities of the system. Its implementation demonstrates the effectiveness of this approach, achieving an impressive Fl-score of up to 90.3 %. This research contributes to the ongoing effort to develop robust and accurate knowledge extraction systems for navigating the ever-expanding landscape of online information. © 2024 IEEE.

关键词： Online systems

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Understanding and Enhancing Issue Prioritization in GitHub 38

Understanding and Enhancing Issue Prioritization in GitHub

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38th IEEE/ACM International conference on Automated software engineering (ASE)

作者： He, Yingying Yang, Wenhua Pan, Minxue Hussain, Yasir Zhou, Yu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350329964

GitHub has become a prominent platform for open source software development, facilitating collaboration and communication among a diverse group of contributors. Efficient issue tracking is a crucial aspect of managing projects on GitHub, and labels serve as one of the primary mechanisms for issue prioritization, while various other issue features are also utilized by issue handlers for the same purpose. However, in large projects, prioritizing issues remains a challenge, and the efficacy of using labels or other issue features for prioritization is not well understood. To address this knowledge gap, we conduct a comprehensive empirical study that investigates the role of labels in GitHub issue prioritization, examines the influence of various issue features on prioritization, and assesses the performance of different ranking algorithms based on these impactful features. Our study, conducted on a dataset comprising data from over 1.5 million issues across diverse GitHub projects, provides valuable insights for issue handling in open source platforms and offers guidance for future research in this domain. Specifically, the study reveals the limited effectiveness of labels in issue prioritization, highlights the significance of certain issue features in the prioritization process, and compares the performance of various ranking algorithms for issue prioritization to support issue handlers.

关键词： GitHub issue tracking issue

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Expert System Development Guidance in Essence Standard

Expert System Development Guidance in Essence Standard

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2023 IEEE International conference on Data and software engineering, ICoDSE 2023

作者： Ibrahim, Mohammad Yahya Widyani, Yani School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350381382

An expert system is a computer system that can imitate the ability of an expert to make decisions and provide solutions and suggestions for a particular problem domain. This research shows the process of building detailed guidance for developing an expert system, particularly a rule-based expert system, to assist people who want to start developing one. Rule-based expert system uses rule to represent knowledge in its knowledge basis. Exploration and selection of expert system development methods were first conducted, followed by method evaluation to identify gaps in each method. The method evaluation generates a list of guidance requirements that are used as a basis for building the initial guidance. Essence was used to build the guidance as a notation to describe terminologies used in software development activity. The constructed guidance was evaluated with a case study by developing an expert system, followed by a qualitative analysis and evidence map analysis. The evidence map analysis tests the relevance of the elements or components defined in the guideline by showing the mapping between the parameters reviewed (guidance elements/components) and the facts that exist (evidence). copy;2023 IEEE. © 2023 IEEE.

关键词： Expert systems

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