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

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Triple Pattern Interfaces for Object-Oriented software APIs

Triple Pattern Interfaces for Object-Oriented Software APIs

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2023 Doctoral Consortium at International Semantic Web conference, ISWC-DC 2023

作者： Weigand, Maximilian Institute of Automation Technology Helmut-Schmidt-University University of the Federal Armed Forces Hamburg Holstenhofweg 85 Hamburg22043 Germany

In multi-disciplinary engineering projects, engineers collaborate and need to exchange information in between different engineering software. Often, the lack of interoperability of software requires engineers to implement complex transfer processes to overcome the heterogeneous syntax and semantics of information. Although knowledge graphs are proven to be a solution to overcome this heterogeneity, engineering software often does not provide interfaces for knowledge graph-based information exchange. To mitigate this lack, this research proposal presents an approach to create virtual knowledge graphs from software-internal data. Following the motivation and importance of the approach, it is broken down into several research questions, as well as research artifacts. The artifacts include software components that virtualize software-internal data as RDF knowledge graphs and make them queryable with SPARQL. Further, other preliminary results including a proof-of-concept implementation are presented. Finally, the further work that is required to evaluate and generalize the approach is specified. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Interoperability

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Encoding Techniques for Handling Categorical Data in Machine Learning-based software Development Effort Estimation 15

Encoding Techniques for Handling Categorical Data in Machine...

引用

15th International conference on knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Hosni, Mohamed MOSI Research Team ENSAM University Moulay Ismail of Meknes Meknes Morocco

ISBN: (纸本)9789897586712

Planning, controlling, and monitoring a software project primarily rely on the estimates of the software development effort. These estimates are usually conducted during the early stages of the software life cycle. At this phase, the available information about the software product is categorical in nature, and only a few numerical data points are available. Therefore, building an accurate effort estimator begins with determining how to process the categorical data that characterizes the software project. This paper aims to shed light on the ways in which categorical data can be treated in software development effort estimation (SDEE) datasets through encoding techniques. Four encoders were used in this study, including one-hot encoder, label encoder, count encoder, and target encoder. Four well-known machine learning (ML) estimators and a homogeneous ensemble were utilized. The empirical analysis was conducted using four datasets. The datasets generated by means of the one-hot encoder appeared to be suitable for the ML estimators, as they resulted in more accurate estimation. The ensemble, which combined four variants of the same technique trained using different datasets generated by means of encoder techniques, demonstrated an equal or better performance compared to the single ML estimation technique. The overall results are promising and pave the way for a new approach to handling categorical data in SDEE datasets. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Machine learning

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A RAG-based Feedback Tool to Augment UML Class Diagram Learning 24

A RAG-based Feedback Tool to Augment UML Class Diagram Learn...

引用

ACM/IEEE 27th International conference on Model Driven engineering Languages and Systems: Companion Proceedings (MODELS)

作者： Ardimento, Pasquale Bernardi, Mario Luca Cimitile, Marta Scalera, Michele Univ Bari Aldo Moro Bari Italy Univ Sannio Benevento Italy UnitelmaSapienza Univ Rome Rome Italy

ISBN: (纸本)9798400706226

This paper introduces an advanced functionality designed to facilitate the learning of UML class diagram construction. Built upon an integrated Retrieval Augmented Generation Large Language Model, the functionality provides enriched feedback by leveraging accumulated knowledge. The functionality is implemented in an existing tool named UML Miner, a Visual Paradigm plugin that captures and analyzes student-generated UML diagrams by applying process mining techniques. By offering personalized feedback and continuous support during modeling, the tool aims to enhance learning outcomes and students' engagement.

关键词： Learning UML software Modeling Retrieval Augmented Generation Large Language Model Tool

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases 38

Domain Adaptive Code Completion via Language Models and Deco...

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

作者： Tang, Ze Ge, Jidong Liu, Shangqing Zhu, Tingwei Xu, Tongtong Huang, Liguo Luo, Bin Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanyang Technol Univ Singapore Singapore Huawei Software Engn Applicat Technol Shenzhen Peoples R China Southern Methodist Univ Dept Comp Sci Dallas TX USA

ISBN: (纸本)9798350329964

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose kNM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that kNM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that kNM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.

关键词： domain adaptive code completion retrieval-augment language model

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Enriching the Semantic Representation of the Source Code with Natural Language-based Features from Comments for Improving the Performance of software Defect Prediction 19

Enriching the Semantic Representation of the Source Code wit...

引用

19th International conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Briciu, Anamaria Lupea, Mihaiela Czibula, Gabriela Czibula, Istvan Gergely Department of Computer Science Babeş-Bolyai University Cluj-Napoca Romania

ISBN: (纸本)9789897586965

The present study belongs to the new research direction that aims to improve software defect prediction by using additional knowledge such as source code comments. The fusion of programming language features learned from the code and natural language features extracted from the code comments is the proposed semantic representation of a source code. Two types of language models are applied to learn the semantic features: (1) the pre-trained models CodeBERT and RoBERTa for code embedding and textual embedding;(2) doc2vec model used for both, code embedding and comments embedding. These two semantic representations, in two combinations (only code features and code features fused with comment features), are used separately with the XGBoost classifier in the experiments conducted on the Calcite dataset. The results show that the addition of the natural language features from the comments increases the software defect prediction performance. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Machine learning

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Benefits of Student Engagement in an engineering Fair for Their Professional Development 15

Benefits of Student Engagement in an Engineering Fair for Th...

引用

15th IEEE Global engineering Education conference (IEEE EDUCON)

作者： Nino-Juarez, Elvira Ortiz Rojas, Alicia Minerva Lara Prieto, Vianney Menchaca Torre, Lizette Tecnol Monterrey Sch Sci & Engn Monterrey Mexico

ISBN: (纸本)9798350394023;9798350394030

Studying engineering requires passion and commitment, among other qualities, which motivate students to exercise their knowledge in practical activities and desire more learning. engineering theory prepares students to face the challenges of industrial life. In this article, we explore the results of engineering students' participation in a project exhibition, assessed by evaluators from industry and our university, i.e., professors and industrial partners. The internal and external evaluators assessed a competition and chose a winner. Evaluations and competitions are motivating elements for students. Creating an event with these characteristics allows students to become aware of local or global external realities, and they can contribute to the world with projects that try to satisfy the promulgations of at least one Sustainable Development Goal (SDG). The University offers 23 different engineering programs, a large variety that provides many opportunities to contribute to the outside world. The event consists of four different categories for project registration depending on its offered solution. The four categories are Physical Prototype, software Prototype, Research and Development of Continuous Improvement Proposals, and Products or Services for Technological-based Entrepreneurship. This article analyzes 1) student awareness of sustainable development, 2) the learning experience, and 3) students' motivation to participate in the exhibition. [1]

关键词： Educational Innovation Higher Education Challenge-based Learning STEM careers Student Engagement Exhibitions Projects Relationship between industry and engineering students engineering Fair

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An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry 23

An Empirical Study of Pre-Trained Model Reuse in the Hugging...

引用

45th IEEE/ACM International conference on software engineering (ICSE)

作者： Jiang, Wenxin Synovic, Nicholas Hyatt, Matt Schorlemmer, Taylor R. Sethi, Rohan Lu, Yung-Hsiang Thiruvathukal, George K. Davis, James C. Purdue Univ W Lafayette IN 47907 USA Loyola Univ Chicago Chicago IL USA

ISBN: (纸本)9781665457019

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries.

关键词： software reuse Empirical software engineering Machine learning Deep learning software supply chain engineering decision making Cybersecurity Trust

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QoS Aware Routing Approaches in software Defined Smart Grids 9

QoS Aware Routing Approaches in Software Defined Smart Grids

引用

9th International conference on Computer Science and engineering, UBMK 2024

作者： Demirci, Sedef Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350365887

software-defined n etworking (SDN) is a promising technology for future smart grid (SG) systems, since SGs heavily rely on communication networks. With SDN, communication network part of SGs becomes programmable by abstracting control logic from routing devices and assigning this task to a logically centralized controller. Thus, in software-defined smart grids, traffic flows can be or chestrated and routed easily. A software-defined smart grid serves different types of traffic flows originating from different types of SG applications such as smart metering, distribution automation, and demand response, etc. These heterogeneous traffic h ave d ifferent Q oS requirements in terms of data rate, latency, reliability, and security, etc., Therefore, providing QoS aware routing mechanisms becomes inevitable for software defined s mart grids. In this study, we classify QoS aware routing approaches according to their QoS goals. We review existing works, and discuss algorithmic and mathematical approaches. Finally, we identify open research issues and present future research directions. To the best of our knowledge, this is the first study that focuses QoS aware routing strategies for SDN-based SGs. © 2024 IEEE.

关键词： Smart power grids

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A Networked Controller Platform based On Open-Source Hardware And software 50

A Networked Controller Platform Based On Open-Source Hardwar...

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50th Annual conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Chen, Dong-Liang Xie, Chun-Li College of Mechanical and Electronic Engineering Dalian Minzu University Dalian China

ISBN: (纸本)9781665464543

This paper introduces a novel networked controller platform based on open-source hardware: Arduino UNO, and software: Scilab/Xcos and Arduino IDE(Integrated Development Environment). To accomplish data exchange among the control components, a communication protocol based on UDP(User Data-gram Protocol) is designed. The adoption of open-source software and hardware renders the controller platform advantages in contrast to traditional networked controller platforms, e.g., being low-cost and time-saving in hardware design and implementation. In addition, the introduction of the network makes the controller platform flexible in structure and applicable to home automation and remote control. The background is introduced first, then the design and implementation are detailed, and an experiment is implemented to validate the practicality of the controller platform. At last, the conclusion ends the paper. To the best knowledge of the authors, there is no precedent of a controller platform that combines Scilab/Xcos, UDP and Arduino UNO yet. © 2024 IEEE.

关键词： Networked control systems

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Rule-based Representation Learning for Traditional Chinese Medicine knowledge Graph 21

Rule-Based Representation Learning for Traditional Chinese M...

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21st IEEE/ACIS International conference on software engineering Research, Management and Applications, SERA 2023

作者： Shi, Dongsheng Lin, Feng Li, Yuxun Chen, Qianzhong Lin, Yiying Zhu, Wentao Li, Dongmei Zhang, Xiaoping Beijing Forestry University Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing100083 China

ISBN: (纸本)9798350345889

Traditional Chinese medicine (TCM) has a unique advantage of preventive treatment of diseases, and adopting the concept of early intervention can effectively prevent diseases. Using knowledge graph is an effective way while the knowledge in the field of TCM is huge and messy. However, the structure of the TCM knowledge graph is often relatively sparse, which makes it highly limited. To this end, a rule-based compositional representation learning (RCRL) model is proposed. RCRL uses the implicit rules in the TCM knowledge graph, which solves the problem of poor representation learning due to the sparse structure of the TCM knowledge graph to a certain extent. Extensive experiments are conducted on the TCM knowledge graph and public datasets, and they are compared with other baselines. Experimental results show that RCRL is superior to other baselines, with improved learning accuracy and interpretability, and can be used for various downstream tasks. © 2023 IEEE.

关键词： knowledge graph

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