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检索条件"任意字段=18th International Conference on Software Engineering and Knowledge Engineering"

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Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization 24

Promise and Peril of Collaborative Code Generation Models: B...

引用

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

作者： Chen, Zhi Jiang, Lingxiao Singapore Management Univ Ctr Res Intelligent Software Engn Sch Comp & Informat Syst Singapore Singapore

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. the exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly *** study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, and incremental training, showing their potential risks in leaking data. Our findings indicate that the size and diversity of code datasets are pivotal factors influencing the success of collaborative trained code models. We demonstrate that federated learning achieves competitive performance compared to centralized training while offering better data protection, as evidenced by lower memorization ratios in the generated code. However, federated learning can still produce verbatim code snippets from hidden training data, potentially violating data privacy or copyright. Our study further explores the patterns of effectiveness and memorization in incremental learning, emphasizing the importance of the sequence in which individual participant datasets are introduced. Also, we identify the memorization phenomenon of cross-organizational clones as a prevalent challenge in both centralized and federated learning scenarios. Our findings highlight the persistent risk of data leakage during inference, even when training data remains unseen. We conclude with strategic recommendations for practitioners and researchers to optimize the use of multisource datasets, thereby propelling the cross-organizational collaboration forward.

关键词： Collaborative Training Memorization Large Language Model Code Generation

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RAHN: A Reputation Based Hourglass Network for Web Service QoS Prediction 36

RAHN: A Reputation Based Hourglass Network for Web Service Q...

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

作者： Chen, Xia Du, Yugen Tang, Guoxing Luo, Yingwei Ma, Benchi Software Engineering Institute East China Normal University Shanghai200062 China Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China

ISBN: (纸本)1891706594

As the homogenization of Web services becomes more and more common, the difficulty of service recommendation is gradually increasing. How to predict Quality of Service (QoS) more efficiently and accurately becomes an important challenge for service recommendation. Considering the excellent role of reputation and deep learning (DL) techniques in the field of QoS prediction, we propose a reputation and DL based QoS prediction network, RAHN, which contains the Reputation Calculation Module (RCM), the Latent Feature Extraction Module (LFEM), and the QoS Prediction Hourglass Network (QPHN). RCM obtains the user reputation and the service reputation by using a clustering algorithm and a Logit model. LFEM extracts latent features from known information to form an initial latent feature vector. QPHN aggregates latent feature vectors with different scales by using Attention Mechanism, and can be stacked multiple times to obtain the final latent feature vector for prediction. We evaluate RAHN on a real QoS dataset. the experimental results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of RAHN are smaller than the six baseline methods. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Web services

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Build a Dataset of Query-Code Pairs Based on CodeSearchNet for Code Search 36

Build a Dataset of Query-Code Pairs Based on CodeSearchNet f...

引用

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

作者： Xu, Yang Chen, Chaowei School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)1891706594

CodeSearchNet is a widely used dataset of comment-code pairs for training code search models. However, code search models trained on the datasets of comment-code pairs usually have lower performance in real-world applications because of the semantic gap between comments and real queries. the existing datasets of query-code pairs are smaller in size and lower in quality than CodeSearchNet for code search research. In this paper, we build a Python dataset of query-code pairs, named QCSN, from CodeSearchNet using a text generator. We train the text generator by fine-tuning the pre-trained model PLBART on StaQC’s query-code pairs. To address the issue of domain shift between StaQC and CodeSearchNet, we exploit a deep adversarial neural network that trains a feature extractor to extract domain-invariant features. the experimental results show that the MRR of the typical code search model trained on QCSN improves by over 18.5%. the ablation study indicates that the deep adversarial neural network module improves the quality of the generated dataset. Further evaluations reveal that the generated queries closely match the real queries in terms of lexical overlap and semantic similarity, accurately expressing the functionality of the source code. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Structured Query Language

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HACS: An Enhancement Framework for Deep Code Search Benefiting from Hard Negative Samples 36

HACS: An Enhancement Framework for Deep Code Search Benefiti...

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

作者： Song, Qihong Liu, Jianxun Hu, Haize School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)1891706594

Code search aims to retrieve relevant code snippets from large code repositories based on query, promoting code reuse and enhancing software development efficiency. Deep Learning is a powerful approach for code search, in which the hard negative samples within training batches critically impact model performance. However, most existing deep code search models only randomly sample negative samples, resulting in a paucity or complete lack of hard negative samples. To address this limitation, we introduce a novel enhancement framework named HACS to optimize the composition of negative samples within batches, thus enhancing the training effectiveness of deep code search models. the core idea is to increase the count of hard negative samples within the negative samples corresponding to each query in the training batch. Specifically, HACS utilizes deep reinforcement learning techniques for sampling hard negative samples and implements vector-level mixed data augmentation strategy to generate hard negative samples. We evaluated our framework on a public dataset covering six programming languages. Experimental results reveal that HACS significantly improves the code search performance of existing models. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep reinforcement learning

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Security Tools' API Recommendation Using Machine Learning 18

Security Tools' API Recommendation Using Machine Learning

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Sworna, Zarrin Tasnim Sreekumar, Anjitha Islam, Chadni Babar, Muhammad Ali Univ Adelaide Ctr Res Engn Software Technol CREST Adelaide SA Australia Univ Adelaide Sch Comp Sci Adelaide SA Australia Cyber Secur Cooperat Res Ctr Adelaide SA Australia

ISBN: (纸本)9789897586477

Security Operation Center (SOC) teams manually analyze numerous tools' API documentation to find appropriate APIs to define, update and execute incident response plans for responding to security incidents. Manually identifying security tools' APIs is time consuming that can slow down security incident response. To mitigate this manual process's negative effects, automated API recommendation support is desired. the state-of-the-art automated security tool API recommendation uses Deep Learning (DL) model. However, DL models are environmentally unfriendly and prohibitively expensive requiring huge time and resources (denoted as "Red AI"). Hence, "Green AI" considering both efficiency and effectiveness is encouraged. Given SOCs' incident response is hindered by cost, time and resource constraints, we assert that Machine Learning (ML) models are likely to be more suitable for recommending suitable APIs with fewer resources. Hence, we investigate ML model's applicability for effective and efficient security tools' API recommendation. We used 7 real world security tools' API documentation, 5 ML models, 5 feature representations and 19 augmentation techniques. Our Logistic Regression model with word and character level features compared to the state-of-the-art DL-based approach reduces 95.91% CPU core hours, 97.65% model size, 291.50% time and achieves 0.38% better accuracy, which provides cost-cutting opportunities for industrial SOC adoption.

关键词： Security Tools' API Security Orchestration API Recommendation Security Operation Center

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A Multi-variant Benchmark for Microservice Systems in software engineering Research 18th

A Multi-variant Benchmark for Microservice Systems in Softwa...

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18th European conference on software Architecture (ECSA)

作者： Cerny, Tomas Chy, Md Showkat Hossain Arju, Muhmmad Ashfakur Rahman Sooksatra, Korn Abdelfattah, Amr S. Lenarduzzi, Valentina Univ Arizona Syst & Ind Engn Tucson AZ 85721 USA Montana State Univ Comp Sci Bozeman MT 59717 USA Baylor Univ Comp Sci Waco TX 76798 USA Univ Oulu M3S Oulu Finland

ISBN: (纸本)9783031709456;9783031712463

Microservice architecture became the mainstream for cloud-native systems. While many microservice system benchmarks have been introduced to the scientific community, there is still a notable gap since the benchmarks do not offer architectural variants of the same system with identical functionality. For instance, research engaged monoto-micro decomposition, but proposed methods use textbook or tutorial systems. Moreover, different microservice granularities in the system provide different architectural trade-offs. this paper extends an established microservice benchmark with two new variants, including a system monolith and a version with 20 microservices. Equivalent functionality is validated across the three benchmark variants.

关键词： Microservices Benchmarks Mono-to-Micro System Variants Trade-offs software Architecture

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Using virtual manikins to tackle particle transport and inhalation risk assessment under different advanced air distribution methods 18

Using virtual manikins to tackle particle transport and inha...

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18th international conference on Indoor Air Quality and Climate, INDOOR AIR 2024

作者： Murga, Alicia Bale, Rahul Ito, Kazuhide Tsubokura, Makoto Kobe University Graduate School of System Informatics Kobe Japan Riken Center for Computational Sciences Kobe Japan Kyushu University Faculty of Engineering Sciences Kasuga Japan

ISBN: (纸本)9798331306816

this research evaluates three air distribution methods in terms of cross-infection minimization through CFD simulation using interacting virtual manikins and the LES approach. Displacement and stratum presented less particle deposition rate inside the respiratory tract due to their stratified nature. this study aims to communicate with interdisciplinary points of view to develop indoor design in a post-pandemic era. © 2024 18th conference of the international Society of Indoor Air Quality and Climate, INDOOR AIR 2024 - conference Program and Proceedings. All rights reserved.

关键词： Indoor air pollution

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Comparing Targeted and Measured Ventilation and Filtration Rates in Classrooms Using Low-Cost Sensors 18

Comparing Targeted and Measured Ventilation and Filtration R...

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18th international conference on Indoor Air Quality and Climate, INDOOR AIR 2024

作者： Hoskin, Zoe Du, Bowen Mendell, Alexander Al-Musa, Ahmad Nahian, Rafsan Haines, Sarah Siegel, Jeffrey University of Toronto Toronto Canada Human-Oriented Built Environment Lab School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland

ISBN: (纸本)9798331306816

Increased ventilation rates and enhanced air filtration are proven ways to decrease indoor airborne transmission of SARS-CoV-2. this study uses sensors to monitor carbon dioxide (CO2) and fine particulate matter (PM2.5) in university classrooms with upgraded filtration and ventilation, and employs an existing decay-clustering approach to find median decay rates in classrooms. the results indicate that measured outdoor air and filtration removal rates are generally lower than targeted total removal rates, highlighting the importance of in-situ testing to verify the performance of ventilation and filtration measures. © 2024 18th conference of the international Society of Indoor Air Quality and Climate, INDOOR AIR 2024 - conference Program and Proceedings. All rights reserved.

关键词： Indoor air pollution

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Towards an AI-centric Requirements engineering Framework for Trustworthy AI 23

Towards an AI-centric Requirements Engineering Framework for...

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

作者： Ronanki, Krishna Univ Gothenburg Dept Comp Sci & Engn Gothenburg Sweden

ISBN: (纸本)9798350322637

Ethical guidelines are an asset for artificial intelligence(AI) development and conforming to them will soon be a procedural requirement once the EU AI Act gets ratified in the European parliament. However, developers often lack explicit knowledge on how to apply these guidelines during the system development process. A literature review of different ethical guidelines from various countries and organizations has revealed inconsistencies in the principles presented and the terminology used to describe such principles. this research begins by identifying the limitations of existing ethical AI development frameworks in performing requirements engineering(RE) processes during the development of trustworthy AI. Recommendations to address those limitations will be proposed to make the frameworks more applicable in the RE process to foster the development of trustworthy AI. this could lead to wider adoption, greater productivity of the AI systems, and reduced workload on humans for non-cognitive tasks. Considering the impact of some of the newer foundation models like Github Copilot and ChatGPT, the vision for this research project is to work towards the development of holistic operationalisable RE guidelines for the development and implementation of trustworthy AI not only on a product level but also on process level.

关键词： Trustworthy AI EU AI Act Requirements engineering Frameworks AI co-worker Ethical AI Guidelines

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Assembly state detection based on deep learning and object matching 18

Assembly state detection based on deep learning and object m...

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IEEE 18th international conference on Automation Science and engineering (IEEE CASE)

作者： Zhao, Shiwen Wang, Junfeng Li, Wang Liu, Maoding Huazhong Univ Sci & Technol Dept Ind & Mfg Syst Engn Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

To improve assembly quality and efficiency, a method based on deep learning and object matching is proposed to detect missing and wrong parts. An improved YOLO V3 neural network is designed to solve the problem of missing assembly. A small target detection scale and attention module is added to the neural network. the size of prior anchor box is optimized by K-means++ clustering algorithm. For the problem of wrong assembly, the standard assembly state detection template is constructed according to the virtual assembly scene in CAD software, and the 2D detection box of the current assembly object in the scene image is matched with the 2D box in the standard state template based on IoU (Intersection over Union) calculation. the assembly model MONA (a 3D model for the evaluation of manual Assembly tasks), is used to test the proposed method. Experimental results show that this method can accurately locate and identify assembly parts, and effectively detect the missing and wrong parts in the assembly process.

关键词： Deep learning Solid modeling three-dimensional displays Neural networks software algorithms Object detection software

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