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

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ZK-CPABE: Blockchain-based CP-ABE Scheme Via Zero-knowledge Proof 7

ZK-CPABE: Blockchain-based CP-ABE Scheme Via Zero-Knowledge ...

引用

7th International conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2024

作者： Zhao, Jinyi Zhu, Jie Yin, Peng Yu, Yimin Xie, Long School of Logistics and Management Engineering Yunnan University of Finance and Economics Kunming China Division of Science & Technology Administration Yunnan University of Finance and Economics Kunming China Lianjia Primary School Wuhua District Kunming China School of Information Yunnan University of Finance and Economics Kunming China

ISBN: (纸本)9781510681866

The integration of blockchain technology with Ciphertext-Policy Attribute-based Encryption (CP-ABE) has addressed key challenges in distributed systems, such as mutual trust and collusion, while enhancing auditability of key management. However, existing blockchain-based CP-ABE models face a trilemma, unable to achieve decentralization, scalability, and security concurrently. Therefore, we propose ZK-CPABE, which merges Zero-knowledge Proofs with blockchain-based CP-ABE, significantly improving verifiability and scalability. This system combines a secure private key distribution and zero-knowledge proofs to construct off-chain computation and scalable transactions for CP-ABE. Our Ethereum-based ZK-CPABE prototype demonstrates its effectiveness and potential for secure, scalable data sharing and access control. © 2024 SPIE.

关键词： Blockchain

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Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning Approach 24

Towards Synthetic Trace Generation of Modeling Operations us...

引用

39th ACM/IEEE International conference on Automated software engineering (ASE)

作者： Muttillo, Vittoriano Di Sipio, Claudio Rubei, Riccardo Berardinelli, Luca Dehghani, MohammadHadi Univ Teramo Teramo Italy Univ Aquila Laquila Italy Johannes Kepler Univ Linz Linz Austria

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Producing accurate software models is crucial in model-driven software engineering (MDE). However, modeling complex systems is an error-prone task that requires deep application domain knowledge. In the past decade, several automated techniques have been proposed to support academic and industrial practitioners by providing relevant modeling operations. Nevertheless, those techniques require a huge amount of training data that cannot be available due to several factors, e.g., privacy issues. The advent of large language models (LLMs) can support the generation of synthetic data although state-of-the-art approaches are not yet supporting the generation of modeling operations. To fill the gap, we propose a conceptual framework that combines modeling event logs, intelligent modeling assistants, and the generation of modeling operations using LLMs. In particular, the architecture comprises modeling components that help the designer specify the system, record its operation within a graphical modeling environment, and automatically recommend relevant operations. In addition, we generate a completely new dataset of modeling events by telling on the most prominent LLMs currently available. As a proof of concept, we instantiate the proposed framework using a set of existing modeling tools employed in industrial use cases within different European projects. To assess the proposed methodology, we first evaluate the capability of the examined LLMs to generate realistic modeling operations by relying on well-founded distance metrics. Then, we evaluate the recommended operations by considering real-world industrial modeling artifacts. Our findings demonstrate that LLMs can generate modeling events even though the overall accuracy is higher when considering human-based operations. In this respect, we see generative AI tools as an alternative when the modeling operations are not available to train traditional IMAs specifically conceived to support industrial practitioners.

关键词： Data privacy Accuracy Large language models Training data Data models software Regulation Context modeling software engineering Synthetic data

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Log Parsing with Prompt-based Few-shot Learning 23

Log Parsing with Prompt-based Few-shot Learning

引用

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

作者： Le, Van-Hoang Zhang, Hongyu Univ Newcastle Sch Informat & Phys Sci Callaghan NSW Australia Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China

ISBN: (纸本)9781665457019

Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing.

关键词： log parsing few-shot learning prompt-tuning deep learning

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Course Assessment Reform based on Internet and Mobile Internet Technology 24

Course Assessment Reform Based on Internet and Mobile Intern...

引用

7th International conference on Educational Technology Management, ICETM 2024

作者： Lin, Yubei Tan, Mingkui School of Software Engineering South China University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798400717468

Examination and evaluation are effective methods for assessing the effectiveness of teaching and the quality of talent cultivation, which are essential components of the teaching process. Traditional course assessments focus on final assessments through paper-based tests, with grading relying on manual labor, which is inefficient and prone to errors. based on Internet and mobile Internet technologies, this paper proposes a course assessment mechanism that combines both process-oriented and summative evaluations. Considering the large number of students and the multitude of knowledge points in the Introduction to Computer and software engineering course, the smart teaching tool Yuketang is used to implement process-oriented assessments, while the summative evaluations are completed using an online examination system developed by the teaching team. After several rounds of teaching practice, the reform measures have been proven to effectively enhance teaching efficiency and improve the quality of teaching. © 2024 Copyright held by the owner/author(s).

关键词： Curricula

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Design of a Digital Navigation Assistant in EMINUS for People with Visual Disabilities 12

Design of a Digital Navigation Assistant in EMINUS for Peopl...

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12th International conference in software engineering Research and Innovation, CONISOFT 2024

作者： Millan Martinez, Max William Barradas, Virginia Lagunes Rojano-Caceres, Jose Rafael Maestría en Sistemas Computacionales Tecnm Instituto Tecnológico Superior de Xalapa Veracruz Xalapa Mexico Universidad Veracruzana Facultad de Estadística e Informática Veracruz Xalapa Mexico

ISBN: (纸本)9798331532116

Requirements analysis is crucial in software system development. With the growth of Artificial Intelligence (AI)-based solutions, this analysis has gained greater importance to create more robust and accessibility-focused solutions. This study defines the software requirements for designing a virtual assistant that utilizes Natural Language Processing (NLP) to facilitate navigation through voice commands for visually impaired people on the EMINUS educational platform, belonging to the Universidad Veracruzana. The necessary requirements are detailed using Google Cloud Text-to-Speech API, neural networks with Bayesian classification algorithms, Rest API services through ***, JavaScript, and HTML5 for creating a plugin in Google Chrome. © 2024 IEEE.

关键词： software design

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The Progression of Competencies and Dispositions: How do Maturing software Engineers Compare? 54

The Progression of Competencies and Dispositions: How do Mat...

引用

54th IEEE Frontiers in Education conference, FIE 2024

作者： Clear, Tony Clear, Alison Auckland University of Technology Department of Computer Science and Software Engineering Auckand New Zealand School of Computing Eastern Institute of technology Auckland New Zealand

ISBN: (纸本)9798350351507

This research paper describes and extends the outcomes from an in- depth study investigating the difference in the expected skills requirements from junior software engineers to senior software engineers, and reflections on the findings from that study. It is a given that senior software engineers have more experience and skills than junior software engineers. However, a focus on their differing competencies and dispositions provides an enhanced mechanism for comparison. Gaps were identified in assessing 'professional knowledge' as categorized by the IEEE/ACM Computing Curriculum Overview Report (CC2020), and in assessing 'dispositions'. It appeared that the specific scenario of comparing the expected competencies between junior and senior software engineers, tested the framework for assessing competencies developed in the CC2020 project and applied in its mapping to the IEEE/ACM Computer Science (CS2013) approved curriculum. In this study into the difference between Junior and Senior software Engineers, an initial review of relevant literature was conducted. The review found that research analyzing job requirements for software engineers of different levels was limited;'experience' as a keyword was seldom mentioned;and a common distinction was made between 'soft' and 'hard' skills - the latter being skills that were 'technical', such as programming languages, frameworks, libraries, and tools, whereas soft skills referred to skills such as personality traits, attitudes, and teamwork skills. In our extension of that work the notion of soft skills was unpacked into professional skills and dispositions. The process of mapping from the CC2020 competency framework to the CS2013 curriculum had deliberately modelled how to represent a competency-based rather than a knowledge-based curriculum. The critical deficiency identified here was the limitation imposed by adopting a skills framework based on the cognitive taxonomy, and thereby unwittingly omitting the crucial compani

关键词： Mapping

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Marker-based AR Application for Better Understanding of Electrical Circuits and Capacitors 4

Marker-Based AR Application for Better Understanding of Elec...

引用

4th International conference on Sustainable Expert Systems, ICSES 2024

作者： Poddar, Kishan Sharma, Bhanu Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9798331540364

This paper proposes a marker-based Augmented Reality (AR) electric circuit meant to help students grasp the knowledge of electrical circuits, especially the usage of capacitors. Recognizing components like the battery, switch, capacitor and bulb, the AR software allows students to engage with real-time representations of electrical activity including capacitor charging and discharging. The interesting and dynamic AR environment helps abstract ideas to be more interactive and accessible, therefore improving knowledge. Early evidence indicates that, when compared to conventional teaching methods, the AR tool greatly increases student attention and knowledge even if there are some technical limitations such inconsistent marker recognition problems. Positive remarks suggest that AR may be a helpful tool for questioning concepts in electrical engineering. Future initiatives will focus on enhancing the technical aspects of the system and extending its contents to provide a more whole learning experience in numerous educational situations. © 2024 IEEE.

关键词： Capacitor bank

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GSAGE2defect: An Improved Approach to software Defect Prediction based on Inductive Graph Neural Network 35

GSAGE2defect: An Improved Approach to Software Defect Predic...

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

作者： Ma, Ju Sun, Yi-Yang He, Peng Zeng, Zhang-Fan School of Computer Science and Information Engineering Hubei University Wuhan China School of Cyber Science and Technology Hubei University Wuhan China

Graph neural network is an effective deep learning framework for learning graph data. Existing research has introduced different variants of graph neural networks into the field of software defects and has achieved promising results. However, the graph neural network model based on the previous research is essentially transductive, is applied to a single fixed graph, and often ignores the direction and weight of the edges when modeling the network. In practice, software systems are dynamically evolving. Furthermore, in software network modeling, the direction and weight of edges are factors that are worth considering. based on an inductive graph neural network, we proposed an improved defect prediction method named GSAGE2defect. We first constructed the class dependency network of the program and then used node2vec for embedding learning to automatically obtain the structural features of the network. Then we combined the learned structural features with traditional software code features to initialize the properties of nodes in the class dependency network. Next, we fed the dependency network to GraphSAGE for a deeper class representation. Finally, we evaluated the proposed method based on eight open-source programs and demonstrated that GSAGE2defect achieves an average improvement of 2.09%-26.69% over state-of-the-art methods in terms of F-measure. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Graph neural networks

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Mapping Learning Objectives of Project-based Undergraduate software engineering Courses to CC2020 Competency Model 53

Mapping Learning Objectives of Project-Based Undergraduate S...

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53rd IEEE ASEE Frontiers in Education International conference, FIE 2023

作者： Suleiman, Ahmad D. Hou, Daqing Liu, Yu Dewaters, Jan Small, Mary M. De Souza, Juliana G. Shepherd, David Clarkson University Electrical & Computer Eng PotsdamNY United States Inst for Stem Education Clarkson University PotsdamNY United States Virginia Commonwealth Univ. Department of Computer Science RichmondVA United States Louisiana State University Department of Computer Science Baton RougeLA United States

ISBN: (纸本)9798350336429

This qualitative research performs a thematic analysis of the learning objectives in existing project-based undergraduate software engineering courses to align them with the competency model defined in the Computing Curricula 2020 reports (CC2020). This study identifies the trends, strengths, and gaps in how the reviewed course learning objectives cover the knowledge, skill, and disposition components of the CC2020 competency model. The learning objectives were categorized according to knowledge elements, skills, and dispositions as defined in the CC2020 competency model. Our analysis shows that 54% of knowledge elements from the reviewed learning objectives do not have any skill level specified and overall, only two out of the eleven dispositions in CC2020 are specified ('Collaboration' and 'Professional'). We also find that technical knowledge elements from the software development category (e.g., software process, software design, and software quality, verification & validation) and systems modeling category (e.g., systems analysis & design, and requirements analysis and specification), probably unsurprisingly, are covered the most often. Similarly, collaboration & teamwork, and oral & written communication are unsurprisingly the most common professional & foundational knowledge elements in the reviewed course's learning objectives as they are essential to project-based learning. Although they are essential for the completion of a successful software project, knowledge elements such as time management, security technology & implementation, and user experience design are rarely mentioned. We discuss the implications of our findings on course design. © 2023 IEEE.

关键词： CC2020 Competency Learning Objectives Learning Outcomes Project-based learning software engineering

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Understanding Code Change with Micro-Changes 40

Understanding Code Change with Micro-Changes

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40th International conference on software Maintenance and Evolution

作者： Chen, Lei Lanza, Michele Hayashi, Shinpei Tokyo Inst Technol Sch Comp Tokyo Japan Univ Svizzera Italiana Software Inst Lugano Switzerland

ISBN: (纸本)9798350395693;9798350395686

A crucial activity in software maintenance and evolution is the comprehension of the changes performed by developers, when they submit a pull request and/or perform a commit on the repository. Typically, code changes are represented in the form of code diffs, textual representations highlighting the differences between two file versions, depicting the added, removed, and changed lines. This simplistic representation must be interpreted by developers, and mentally lifted to a higher abstraction level, that more closely resembles natural language descriptions, and eases the creation of a mental model of the changes. However, the textual diff-based representation is cumbersome, and the lifting requires considerable domain knowledge and programming skills. We present an approach, based on the concept of micro-change, to overcome these difficulties, translating code diffs into a series of pre-defined change operations, which can be described in natural language. We present a catalog of micro-changes, together with an automated micro-change detector. To evaluate our approach, we performed an empirical study on a large set of open-source repositories, focusing on a subset of our micro-change catalog, namely those related to changes affecting the conditional logic. We found that our detector is capable of explaining more than 67% of the changes taking place in the systems under study.

关键词： software evolution source code mining diff analysis

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