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

共 17413 条记录，以下是181-190 订阅

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Rethinking software engineering in the Era of Foundation Models A Curated Catalogue of Challenges in the Development of Trustworthy FMware 32

Rethinking Software Engineering in the Era of Foundation Mod...

引用

32nd ACM International conference on the Foundations of software engineering, FSE Companion

作者： Hassan, Ahmed E. Lin, Dayi Rajbahadur, Gopi Krishnan Gallaba, Keheliya Cogo, Filipe Roseiro Chen, Boyuan Zhang, Haoxiang Thangarajah, Kishanthan Oliva, Gustavo Lin, Jiahuei Abdullah, Wali Mohammad Jiang, Zhen Ming Queen’s University Kingston Canada Huawei Canada York University Canada

ISBN: (纸本)9798400706585

Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built using FMs as FMware. The unique properties of FMware (e.g., prompts, agents and the need for orchestration), coupled with the intrinsic limitations of FMs (e.g., hallucination) lead to a completely new set of software engineering challenges. based on our industrial experience, we identified ten key SE4FMware challenges that have caused enterprise FMware development to be unproductive, costly, and risky. For each of those challenges, we state the path for innovation that we envision. We hope that the disclosure of the challenges will not only raise awareness but also promote deeper and further discussions, knowledge sharing, and innovative solutions. © 2024 Copyright held by the owner/author(s).

关键词： software design

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LLM-based Chatbots for Mining software Repositories: Challenges and Opportunities 24

LLM-Based Chatbots for Mining Software Repositories: Challen...

引用

28th International conference on Evaluation and Assessment in software engineering (EASE)

作者： Abedu, Samuel Abdellatif, Ahmad Shihab, Emad Concordia Univ Montreal PQ Canada Univ Calgary Calgary AB Canada

ISBN: (纸本)9798400717017

software repositories have a plethora of information about software development, encompassing details such as code contributions, bug reports and code reviews. This rich source of data can be harnessed to enhance not only software quality and development velocity but also to gain insights into team collaboration and inform strategic decision-making throughout the software development lifecycle. Previous studies show that many stakeholders cannot benefit from the project information due to the technical knowledge and expertise required to extract the project data. To lower the barrier to entry by automating the process of extracting and analyzing repository data, we explored the potential of using an LLM to develop a chatbot for answering questions related to software repositories. We evaluated the chatbot on 150 software repository-related questions. We found that the chatbot correctly answered one question. This result prompted us to shift our focus to investigate the challenges in adopting LLMs for the out-of-thebox development of software repository chatbots. We identified five main challenges related to retrieving data, structuring the data, and generating the answer to the user's query. Among these challenges, the most frequent (83.3%) is the inaccurate retrieval of data to answer questions. In this paper, we share our experience and challenges in developing an LLM-based chatbot to answer software repository-related questions within the SE community. We also provide recommendations on mitigating these challenges. Our findings will serve as a foundation to drive future research aimed at enhancing LLMs for adoption in extracting useful information from software repositories, fostering advancements in natural language understanding, data retrieval, and response generation within the context of software repository-related questions and analytics.

关键词： software Chatbots Large Language Model Conversational Development Assistant

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

引用

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

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： knowledge based systems

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BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization 24

BTTackler: A Diagnosis-based Framework for Efficient Deep Le...

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30th ACM SIGKDD conference on knowledge Discovery and Data Mining

作者： Pei, Zhongyi Cen, Zhiyao Huang, Yipeng Wang, Chen Liu, Lin Yu, Philip Long, Mingsheng Wang, Jianmin Tsinghua Univ Sch Software BNRist Beijing Peoples R China Tsinghua Univ Sch Software EIRI Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China

ISBN: (纸本)9798400704901

Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different hyperparameter configurations amongst a specific search space. However, many trials may encounter severe training problems, such as vanishing gradients and insufficient convergence, which can hardly be reflected by accuracy metrics in the early stages of the training and often result in poor performance. This leads to an inefficient optimization trajectory because the bad trials occupy considerable computation resources and reduce the probability of finding excellent hyperparameter configurations within a time limitation. In this paper, we propose Bad Trial Tackler (BTTackler), a novel HPO framework that introduces training diagnosis to identify training problems automatically and hence tackles bad trials. BTTackler diagnoses each trial by calculating a set of carefully designed quantified indicators and triggers early termination if any training problems are detected. Evaluations are performed on representative HPO tasks consisting of three classical deep neural networks (DNN) and four widely used HPO methods. To better quantify the effectiveness of an automated HPO method, we propose two new measurements based on accuracy and time consumption. Results show the advantage of BTTackler on two-fold: (1) it reduces 40.33% of time consumption to achieve the same accuracy comparable to baseline methods on average and (2) it conducts 44.5% more top-10 trials than baseline methods on average within a given time budget. We also released an open-source Python library that allows users to easily apply BTTackler to automated HPO processes with minimal code changes.

关键词： hyperparameter optimization training diagnosis deep neural network

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Towards Human-Bot Collaborative software Architecting with ChatGPT 23

Towards Human-Bot Collaborative Software Architecting with C...

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27th International conference on Evaluation and Assessment in software engineering (EASE)

作者： Ahmad, Aakash Waseem, Muhammad Liang, Peng Fahmideh, Mahdi Aktar, Mst Shamima Mikkonen, Tommi Lancaster Univ Leipzig Sch Comp & Commun Leipzig Germany Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland Wuhan Univ Sch Comp Sci Wuhan Peoples R China Univ Southern Queensland Sch Business Darling Hts Qld Australia

ISBN: (纸本)9798400700446

Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) suffers from a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software. software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT to architect a service-based software. Future research focuses on harnessing empirical evidence about architects' productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.

关键词： software Architecture ChatGPT Large Language Models DevBots

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A knowledge Push Method to Support the Debugging Process Design of Complex Electronic Products 24

A Knowledge Push Method to Support the Debugging Process Des...

引用

8th International conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Xue, Hao Chen, Xingyu Luo, Tiannan Li, Yang Liu, Yuhan Zhang, Kai School of Mechanical Engineering Sichuan University Sichuan Chengdu China Southwest China Research Institute of Electronic Equipment Sichuan Chengdu China

ISBN: (纸本)9798400710094

The core of design lies in the acquisition and application of knowledge. knowledge push technology can effectively improve the utilization efficiency of knowledge by designers, thus enabling more efficient task completion. To address the current challenges in debugging process design, including insufficient knowledge management and the dynamic demand for knowledge, this paper proposes a knowledge push method tailored for the debugging process design of complex electronic products. By constructing a knowledge graph for debugging processes and utilizing a semantic similarity-based push algorithm, the method intelligently identifies knowledge needs during the design process, achieving precise knowledge delivery. On this basis, a prototype system supporting knowledge push for complex electronic product debugging design was developed. Taking the debugging process design of an elevator drive system as an example, the method has been proven to effectively improve design efficiency. © 2024 Copyright held by the owner/author(s).

关键词： knowledge graph

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Domain knowledge Graph Completion based on Attribute Hierarchy 7

Domain Knowledge Graph Completion Based on Attribute Hierarc...

引用

7th International conference on Electronic Information Technology and Computer engineering (EITCE)

作者： Lan, Ning Yang, Shuqun Software Engn Inst Guangzhou Dept Comp Sci Guangzhou 510980 Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China

ISBN: (纸本)9798400708305

knowledge graph completion is to solve the problem of lack of entities and relations in knowledge graphs. Existing knowledge graph completion methods mainly embed entities and relations into latent vectors, and numerous researches have taken the rich relationships in knowledge graph into account such as category, entity, relation and semantic. However, only a few studies consider the relation between attributes, which is the basis of describing the entity. This paper proposes the Attribute Hierarchy knowledge Graph Completion (AH-KGC) method, aiming at leveraging the attribute relation to find the missing obligatory property of entities. Primarily, in AH-KGC, we have discussed the attributes prerequisite relation, which can be described as a tree-like hierarchical structure, and then adopt the search algorithm of preorder traversal based on the hierarchy to find out the missing attributes. Specifically, we prove that attribute prerequisite is a special case of implication, thus can obtain attribute hierarchy from implications relation, which can easily be obtained in much mature research such as expert systems and make full use of the knowledge in various fields to make up for the vacancy of domain knowledge in the knowledge graph. The experiment has been performed on the CN-DBpedia and Probase datasets. The result demonstrates that AH-KGC can effectively complete the missing attributes of entities in knowledge graphs and achieve 100% accuracy under our evaluation system.

关键词： knowledge graph completion attribute hierarchy implication relation

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A software Defect Prediction Method based on Multi-type Features and Feature Selection 35

A Software Defect Prediction Method based on Multi-type Feat...

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

作者： Liu, Jiaxin Xu, Hao Lu, Lu Zou, Quanyi Yang, Zhanyu School of Computer Science and Engineering South China University of Technology Guangzhou China School of Journalism and Communication South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China

Numerous software defect prediction methods utilize semantic information and software metrics as code features, neglecting the structural knowledge inherent in the source code. Other studies improve feature completeness by simply combining different types of defect indicators, which causes information redundancy. To address these challenges, this paper proposes a novel software defect prediction method that incorporates multi-type features and performs feature selection. Firstly, semantic and structural features are extracted by Text Convolutional Neural Network (TextCNN) and Graph Isomorphism Network (GIN) from Abstract Syntax Tree (AST) and Program Dependency Graph (PDG), respectively, which are combined with software metrics to build a multi-type feature set. Then, Recursive Feature Elimination with Cross-Validation (RFECV) integrating a novel feature importance measure is utilized to remove redundant features and generate a feature subset. Finally, a prediction model for classification is established based on the feature subset. The experiments validated the effectiveness of multi-type features and the improved RFECV. Overall our proposed method outperforms state-of-the-art techniques on nine Java open-source projects. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Abstracting

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Cloud Programming Languages and Infrastructure from Code: An Empirical Study 17

Cloud Programming Languages and Infrastructure from Code: An...

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17th ACM SIGPLAN International conference on software Language engineering (SLE)

作者： Simhandl, Georg Zdun, Uwe Univ Vienna Fac Comp Sci Res Grp Software Architecture Vienna Austria

ISBN: (纸本)9798400711800

Infrastructure-from-Code (IfC) is a new approach to DevOps and an advancement of Infrastructure-as-Code (IaC). One of its key concepts is to provide a higher level of abstraction facilitated by new programming languages or software development kits, which automatically generate the necessary code and configurations to provision the infrastructure, deploy the application, andmanage the cloud services. IfC approaches promise higher developer productivity by reducing DevOps-specific tasks and the expert knowledge required. However, empirical studies on developers' performance, perceived ease of use, and usability related to IfC are missing. We conducted a controlled experiment (n=40) to assess the usability of the cloud programming languages (PL) and software development kits (SDK). Both approaches involve similar effectiveness. We found that the PL-based approach was moderately less efficient but increased correctness with time spent on programming. Tracing generated infrastructure configurations from code was more challenging with the SDK-based approach. Applying thematic analysis, 19 themes emerged related to usability barriers, supporting factors, security, cloud cost, and enhancement areas. We conclude with five findings and future directions.

关键词： Programming Language Cloud Infrastructure From Code Empirical Study Experiment

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2024 14th International conference on Computer and knowledge engineering, ICCKE 2024

2024 14th International Conference on Computer and Knowledge...

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14th International conference on Computer and knowledge engineering, ICCKE 2024

ISBN: (纸本)9798331511272

The proceedings contain 79 papers. The topics discussed include: an interactive approach for query-based multi-document scientific text summarization;enhancing Persian word sense disambiguation with large language models techniques and applications;assessing users' influence on respondents in conversation quality: a quantitative study on reddit based on the cooperative principle;non-negative matrix factorization improves residual neural networks;cluster sampling: a cluster-driven sampling strategy for deep metric learning;a scalable blockchain-based educational network for data storage and assessment;towards efficient capsule networks through approximate squash function and layer-wise quantization;automated software design using machine learning with natural language processing;evaluation of efficient electrocardiomatrix-based identification using deep learning methods;disturbance rejection in quadruple-tank system by proposing new method in reinforcement learning;and an improved and accurate measure for mining correlated high-utility itemsets.

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