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检索条件"任意字段=19th International Conference on Software Engineering and Knowledge Engineering"

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Conversational Assistants for software Development: Integration, Traceability and Coordination 19

Conversational Assistants for Software Development: Integrat...

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

作者： Contreras, Albert Guerra, Esther de Lara, Juan Computer Science Department Universidad Autónoma de Madrid Spain

ISBN: (纸本)9789897586965

the recent advances in generative artificial intelligence are revolutionising our daily lives. Large language models (LLMs) – the technology underlying conversational agents like ChatGPT – can produce sensible text in response to user prompts, and so, they are being used to solve tasks in many disciplines like marketing, law, human resources or media content creation. software development is also following this trend, with recent proposals for conversational assistants tailored for this domain. However, there is still a need to understand the possibilities of integrating these assistants within integrated development environments (IDEs), coordinating multiple assistants, and tracing their contributions to the software project under development. this paper tackles this gap by exploring alternatives for assistant integration within IDEs, and proposing a general architecture for conversational assistance in software development that comprises a rich traceability model of the user-assistant interaction, and a multi-assistant coordination model. We have realised our proposal building an assistant (named CARET) for Java development within Eclipse. the assistant supports tasks like code completion, documentation, maintenance, code comprehension and testing. We present an evaluation for one specific development task (method renaming), showing promising results. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： software design

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Web Systems for the Development of Computational thinking in software engineering Students: A Systematic Literature Review 11

Web Systems for the Development of Computational Thinking in...

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11th international conference in software engineering Research and Innovation (CONISOFT)

作者： Pale-Parra, Daniel Perez-Salazar, Christian Sanchez-Garcia, Angel J. Ocharan-Hernandez, Jorge Octavio Univ Veracruzana Fac Estadist & Informat Xalapa Mexico

ISBN: (纸本)9798350328837;9798350328844

Many undergraduate students in software engineering have trouble developing computational thinking. Several tools have been reported in the literature to support the development of computational thinking. this paper reports a Systematic Literature Review to present the characteristics that have made successful web systems that support the development of computational thinking in recent years and the reported limitations. Eighteen primary studies were selected where the strategies used are usually learning through lessons, practicing with exercises, working through games, and using feedback. the systems found have been used at different elementary, middle, high school, and bachelor's degree levels. Finally, the main limitations reported in using these systems were mainly the difficulty of the topics, the previous knowledge that the students should have, the attitude that the students showed and the lack of motivation. Finally, we make some recommendations to software engineering curriculum planners and programming teachers about systems for developing computational thinking.

关键词： Computational thinking web system Learning software engineering students

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Using Context and Hierarchy Features to Enhance Code Completion in Code Modification Scenarios 36

Using Context and Hierarchy Features to Enhance Code Complet...

引用

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

作者： Li, Linhai Xing, Mingjie Li, Ling Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing211135 China Nanjing Institute of Software Technology Nanjing China

ISBN: (纸本)1891706594

Automated software development has always been a research hotspot in the field of software engineering, and code completion can be regarded as a key technology. Currently, many studies on code completion regard the context of prediction as code that strictly appears before the cursor, and have not fully considered the use of code around the cursor for code modification scenarios. Meanwhile, the code serialization method has been developed from source code serialization to Abstract Syntax Tree (AST) serialization. However, AST serialization may lose original structure information. To solve these problems we propose contextual AST serialization (CAS) method, which can more accurately predict AST nodes by capturing the semantic association, context information and structure information between AST nodes. CAS not only converts AST nodes into sequences, but also connects AST sequences with the surrounding context to form richer representations. we conduct empirical studies using a standard Python dataset. the results demonstrate that CAS serialization method significantly improves the code prediction accuracy of the model, the MRR of various types of next token predictions is improved by 15.8% compared to TravTrans on average, and that of type node is improved by 19.4% on average. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Origami Infrastructure: A Viable Solution to Construction for Challenging Environments 19

Origami Infrastructure: A Viable Solution to Construction fo...

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19th Biennial international conference on engineering, Science, Construction, and Operations in Challenging Environments, Earth and Space 2024

作者： Müller, Claudia Calle Bordo, Alvaro Ballón Elzomor, Mohamed Dept. of Civil and Environmental Engineering Florida International Univ United States Toronto ON Canada Moss School of Construction Infrastructure and Sustainability College of Engineering and Computing Florida International Univ. MiamiFL United States

ISBN: (数字)9780784485736

ISBN: (纸本)9780784485736

Infrastructure built in challenging environments should be able to withstand unforeseen climate and environmental challenges, including extreme temperatures, high winds, severe rainfall, and natural disasters. Most importantly, dwellings should provide humans with a safe, livable, and resilient shelter. To this end, construction materials, design solutions, and construction processes must be considered to ensure safe and livable conditions. Furthermore, technology and innovative means of construction must be explored to reinforce infrastructure resilience and sustainable performance. However, previous literature has not addressed which features and materials are most adequate for resilient, deployable, and cost-effective origami shelter structures. this research aims to fill this gap by (1) identifying the suitable materials and features of structures that have been proven to be effectively deployable in such challenging environments through literature review;and (2) proposing and analyzing the viability of origami infrastructure as a solution to construction obstacles in challenging environments. Origami is rapidly emerging in science, engineering, and construction applications as deployable and reconfigurable engineering systems of all scales that can be fabricated with a wide variety of materials. Origami structures are geometrically versatile, adaptable, and can be easily and quickly assembled. Moreover, they are lightweight and foldable, which facilitates their transportation into challenging environments where access can be difficult and limited. therefore, origami structures present an innovative, sustainable, resilient, and feasible solution to construction obstacles in challenging environments. the findings of this study serve researchers and construction stakeholders who will be designing and building infrastructure systems in challenging environments. © ASCE.

关键词： Structural Mechanics

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Prompt Patterns for Agile software Project Managers: First Results 14th

Prompt Patterns for Agile Software Project Managers: First R...

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14th international conference on software Business (ICSOB)

作者： Sainio, Kari Abrahamsson, Pekka Ahtee, Tero Univ Tampere Tampere 33100 Finland

ISBN: (纸本)9783031532269;9783031532276

In the evolving field of Agile Project Management (APM), the role of the project manager is in transition. this paper identifies common 'pain points' in APM through a literature review and constructs a theoretical model to address them. the study introduces 'Prompt engineering' as a novel approach to leverage artificial intelligence (AI), specifically ChatGPT, for mitigating these challenges. Empirical research evaluates ChatGPT's capabilities and reliability in managing various project tasks using engineered prompts. the findings suggest that while ChatGPT cannot fully replace human project managers, it excels in assisting, guiding, and automating specific tasks when guided by well-crafted prompts. As an outcome, prompt engineering patterns for project managers is proposed to facilitate the application of AI in agile settings. In this paper, we introduce patterns for requirements management, stakeholder and management teams and role clarification. the paper concludes that ChatGPT's knowledge is generally reliable but emphasizes the need for expert evaluation in critical areas.

关键词： Agile Project Management Pain Points Artificial Intelligence LLM ChatGPT Prompt engineering Patterns

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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...

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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...

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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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Draw a software Engineer Test - An Investigation into Children's Perceptions of software engineering Profession 23

Draw a Software Engineer Test - An Investigation into Childr...

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45th IEEE/ACM international conference on software engineering - software engineering in Society (ICSE-SEIS)

作者： Cutrupi, Claudia Maria Zanardi, Irene Jaccheri, Letizia Landoni, Monica Norwegian Univ Sci & Technol Dept Comp Sci IDI Trondheim Norway Univ Svizzera Italiana Fac Informat Lugano Switzerland

ISBN: (纸本)9798350322613

Context: the gender gap is particularly affecting the software engineering community, as both academia and industry are dominated by men. Literature reports how the lack of women is a consequence of gender stereotypes around certain figures that begin in the early stages of education, affecting children's perceptions of the role they can play across scientific fields. Objective: In this study, we asked children to draw a software engineer in order to collect their perceptions and let us check whether gender stereotypes still persist. Methods: We asked a total of 371 children to draw a person who works in the software engineering field. We analyzed the drawings based on a set of parameters extracted from literature and inspected the results through a cross-sectional study. Results: Children agreed on their representations of a software engineer: 51% drew a man and 44% drew a woman, while 5% a non-recognizable figure. the main differences emerged when the data were grouped by age and gender: only 23% of eleven-year-old girls drew a woman software engineer, while 54% drew a man, and in 23% gender was non-recognizable. Conclusion: the findings revealed a favorable gender balance in children's perceptions of software engineering. they seem more willing to recognize diversity, an improvement compared with what was reported in previous studies. Children's perceptions of technology may have become more accessible as a result of the COVID-19 situation. these findings may draw positive comparisons with the current gender gap in software engineering, encouraging future developments.

关键词： gender stereotypes children's drawings software engineering draw-a-computer-scientist test primary school students drawing coding

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