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

共 17414 条记录，以下是141-150 订阅

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An Evolvable knowledge Graph Supporting a Hybrid Intelligence Autonomous Driving System

An Evolvable Knowledge Graph Supporting a Hybrid Intelligenc...

引用

IEEE International conference on software Architecture Workshops (ICSAW)

作者： Anna Teern Nada Elgendy Pertti Seppänen Tero Päivärinta Empirical Software Engineering in Software Systems and Services University of Oulu Oulu Finland

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Route planning is influenced by various factors, with speed and distance often prioritized in map services. However, dynamic conditions such as road maintenance, weather, or accidents can significantly impact these routes. By leveraging hybrid intelligence (HI), the collaboration between human intuition and machine efficiency, we focus on supporting decision-making in autonomous and semi-autonomous driving contexts. This study explores integrating knowledge of the dynamic conditions into HI Autonomous Driving Systems (HI-ADS) within the 6G Visible Project. The findings demonstrate the potential of knowledge graphs (KGs) to enhance decision-making by integrating evolving data and ensuring adaptability to real-world driving conditions. based on the design objectives of learning for evolvable KGs, concrete requirements for the HI-ADS KG are established.

关键词： Technological innovation software architecture Smart cities Roads Decision making knowledge graphs Ontologies Planning Autonomous vehicles Open data

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Design Matrix Factorization and knowledge-Guided Clustering for Trust-Aware Cross-Domain Recommendation Systems

Design Matrix Factorization and Knowledge-Guided Clustering ...

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software engineering and Computer Science (CSECS), International conference on

作者： Jiaqi Chang Fusheng Yu School of Mathematical Sciences Beijing Normal University Beijing People's Republic of China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The core challenge in designing an algorithm for trust-aware cross-domain recommendation systems lies in effectively integrating ratings from all domains (target-domain and source-domains) and trust relationships to improve recommendation performance in the target-domain. These systems involve two types of information fusion: intra-domain information fusion, which integrates intra-domain trust relationships and ratings within each domain (target-domain or source-domain), and cross-domain information fusion, which fuses information from all domains through inter-domain trust relationships as a bridge. The existing algorithms are limited in that they only address either intra-domain information fusion or cross-domain information fusion, failing to comprehensively utilize both intra-domain and inter-domain trust relationships for integrating information from all domains. To address this limitation, we propose a novel approach that combines inter-domain information fusion and cross-domain information fusion. based on this approach, we design a matrix factorization and knowledge-guided clustering based trust-aware cross-domain collaborative filtering algorithm (MFKC _ TCCFA). Experimental results on a dataset show that MFKC_TCCFA outperforms existing state-of-the-art algorithms.

关键词： Computer science Social networking (online) Fuses Collaborative filtering Soft sensors software algorithms Semantics Clustering algorithms Recommender systems software engineering

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Proceedings - Asia-Pacific software engineering conference, APSEC

Proceedings - Asia-Pacific Software Engineering Conference, ...

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1st Asia-Pacific software engineering conference, APSEC 1994

ISBN: (纸本)0818669608

The proceedings contain 45 papers. The topics discussed include: optimal release policies for hyper-geometric distribution software reliability growth model with scheduled delivery time;an approach to predict software maintenance cost based on ripple complexity;prescriptive metrics for software quality assurance;software information management system based on the entity-relationship model;a hybrid program knowledge base for static program analyzers;object-oriented analysis and design support system using algebraic specification techniques;when to inherit and when not to;and a sentential function mapping method for object-oriented analysis and design.

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On the Performance of Large Language Models for Code Change Intent Classification

On the Performance of Large Language Models for Code Change ...

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IEEE International conference on software Analysis, Evolution and Reengineering (SANER)

作者： Issam Oukhay Moataz Chouchen Ali Ouni Fatemeh H. Fard ÉTS Montreal QC Canada Concordia University QC Canada University of British Columbia Canada

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Modern Code Review (MCR) is an essential practice in software engineering, supporting early defect detection, enhancing code quality, and fostering knowledge. To manage code review tasks effectively, developers need to understand the intent behind code changes, such as a bug fix, test, refactoring, or new feature. Traditional methods for categorizing code changes in MCR rely on rule-based heuristics with predefined keywords. However, these methods lack context regarding the code changes, leading to limited generalizability, particularly when dealing with sparsely documented changes. This paper addresses these limitations by investigating the potential of Large Language Models (LLMs) for changes' intent classification. We introduce LLM Change Classifier (LLMCC), an LLM-based approach that classifies code changes based on their underlying intent. We evaluate the effectiveness of LLMCC by conducting an empirical study on three open-source projects: Android, OpenS tack, and Qt. The performance of LLMCC was benchmarked against traditional heuristic methods, conventional machine learning algorithms (including Decision Trees and Random Forests), and state-of-the-art transformer models (including BERT and RoBERTa). Results show that LLMCC significantly enhances code change intent classification accuracy, achieving up to a 33 % improvement in F1 score over heuristic-based methods. Additionally, LLMCC outperformed both traditional machine learning and transformer models, achieving an average 77% improvement in terms of Matthew Correlation Coefficient (MCC). These findings underscore the potential of LLMCC to streamline code change intent classification.

关键词： knowledge engineering Codes Machine learning algorithms Intent recognition Reviews Large language models Transformers software Random forests software engineering

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Proceedings - 2014 23rd Australasian software engineering conference, ASWEC 2014

Proceedings - 2014 23rd Australasian Software Engineering Co...

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23rd Australasian software engineering conference, ASWEC 2014

ISBN: (纸本)9781479931491

The proceedings contain 29 papers. The topics discussed include: evaluating technical debt in cloud-based architectures using real options;supporting complex work in crowdsourcing platforms: a view from service-oriented computing;design and implementation of dynamically evolving ensembles with the helena framework;architecture conformance analysis approach within the context of multiple product line engineering;using constraint satisfaction and optimization for pattern-based software design;architectural design decisions in open software development: a transition to software ecosystems;services for knowledge sharing in dynamic business networks;open architectures and software evolution: the case of software ecosystems;concentus: applying stream processing to online collective interaction;and fill that blank! an iOS-based literacy application.

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chiming Duan Tong Jia Yong Yang Guiyang Liu Jinbu Liu Huxing Zhang Qi Zhou Ying Li Gang Huang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%.

关键词： Training Industries Systems operation Large language models Retrieval augmented generation Active learning Noise software systems Speech processing Anomaly detection

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An Ensemble Learning Method based on Neighborhood Granularity Discrimination Index and Its Application in software Defect Prediction

An Ensemble Learning Method Based on Neighborhood Granularit...

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IEEE International conference on software Analysis, Evolution and Reengineering (SANER)

作者： Yuqi Sha Feng Jiang Qiang Hu Yifan He College of Information Science and Technology Qingdao University of Science and Technology Qingdao P. R. China Institute of Cosmetic Regulatory Science Beijing Technology and Business University Beijing P.R. China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

software defect prediction (SDP) is a primary field of study in software engineering, aiming to optimize test resource allocation by highlighting the defect-prone software modules. Over the last few years, ensemble learning method has been extensively adopted in SDP. However, how to strengthen the diversity of base learners is an issue in ensemble learning. In this paper, we consider the problem of diversity in the eye of feature space perturbation. First, we propose the notion of neighborhood granularity discrimination index (NGDI), by combining the neighborhood knowledge granularity with the neighborhood discrimination index within the framework of neighborhood rough sets. NGDI can not only measure the uncertainty of feature subsets' discriminant capability, but also characterize the granularity of neighborhood knowledge induced by feature subsets. Second, we propose an ensemble learning algorithm, EL-NGDI, established on the NGDI. ELNGDI disturbs the feature space using multiple NGDI-based neighborhood approximate reducts. Third, we use ELNGDI to predict software defects. ELNGDI and the Synthetic Minority Oversampling Technique (SMOTE) are combined in order to handle the class imbalance issue in SDP, and propose a mechanism called SMOTE-ELNGDI. Experimental results on 20 datasets demonstrate that ELNGDI effectively improves the performance of SDP compared with existing ensemble learning methods.

关键词： Uncertainty software algorithms Diversity reception Rough sets Prediction algorithms Approximation algorithms software Ensemble learning Indexes software engineering

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An Enhanced Keylogger Detection Systems Using Recurrent Neural Networks Enabled with Feature Selection Model 7th

An Enhanced Keylogger Detection Systems Using Recurrent Neur...

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7th International conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Rodríguez, Demóstenes Zegarra Akanji, Boluwatife Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Computer Science Federal University of Lavras Minas Gerais Lavras Brazil

ISBN: (纸本)9789819764648

Keyloggers are malicious software programs that record keystrokes of users without their consent or knowledge. They can steal sensitive information like credit card numbers and passwords. They pose a significant threat to users’ privacy and security since they capture keystroke data. Keylogger detection systems play a vital role in safeguarding users from cyberattacks and mitigating the potential harm caused by keyloggers. Keylogger detection systems employing deep learning have been widely used for identifying and mitigating cyber threats associated with keyloggers. However, they struggle to identify new or unfamiliar keylogger samples. This study aims to explore deep learning techniques with feature selection model to detect keylogger. This study employs Recurrent Neural Networks (RNNs) using a collection of known keylogger dataset. Furthermore, a correlation-based feature extraction method was applied to identify the most relevant features for the model, highlighting the importance of specific features, such as URG flag Count, ACK flag count, and Idle Mean, in differentiating between benign and keylog classes. The proposed model demonstrates superior accuracy of 0.8763 and precision of 0.8569 compared to a baseline model using Logistic Regression (LR) 0.7721 and 0.7407, respectively, indicating a better balance between accurate classification and minimizing false negatives and false positives. The findings from this study help to improve keylogger detection methods, making them more powerful and efficient. These results suggest that a keylogger-based detection system employing a deep learning approach can be a valuable tool for addressing the complex and evolving landscape of cyber threats related to keylogging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Proceedings of the 11th IASTED International conference on software engineering and Applications, SEA 2007

Proceedings of the 11th IASTED International Conference on S...

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11th IASTED International conference on software engineering and Applications, SEA 2007

ISBN: (纸本)9780889867055

The proceedings contain 93 papers. The topics discussed include: software quality model based on software development approaches;effects of dependency injection on maintainability;improved decision-making for software managers using Bayesian networks;calibrated estimation model for a maintenance project;a new model for evaluating performability under the effects of software aging and rejuvenation;towards a compliance support framework for global software companies;improving the quality of knowledge representation for requirements engineering through natural language requirements patterns;empirical evaluation of issue based variability modeling using the experimental survey technique;capturing behavior coordination in goal-oriented requirement engineering;and considering environmental function in reliability growth modeling from testing to operation.

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Proceedings - 2018 6th International conference in software engineering Research and Innovation, CONISOFT 2018

Proceedings - 2018 6th International Conference in Software ...

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6th International conference in software engineering Research and Innovation, CONISOFT 2018

ISBN: (纸本)9781538665770

The proceedings contain 22 papers. The topics discussed include: SagaMAS: a software framework for distributed transactions in the microservice architecture;source code metrics to predict the properties of FPGA/VHDL-based synthesized products;what is programming? putting all together - a set of skills required;B-learning in the teaching-learning of Boolean function simplification;adaptation of the initial software development method for a single developer;ROKA a software development methodology for industrial automation;design of experiments applied to a software engineering project based on knowledge processes;and how to select the appropriate pattern of human-computer interaction?: a case study with junior programmers.

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