arXiv - CS - Software Engineering最新文献_第8页

Choosing the Right Communication Protocol for your Web Application 为网络应用程序选择正确的通信协议

arXiv - CS - Software Engineering Pub Date : 2024-09-11 DOI: arxiv-2409.07360

Mohamed Hassan

{"title":"Choosing the Right Communication Protocol for your Web Application","authors":"Mohamed Hassan","doi":"arxiv-2409.07360","DOIUrl":"https://doi.org/arxiv-2409.07360","url":null,"abstract":"Selecting the appropriate communication protocol is crucial for optimizing\u0000the performance, scalability, and user experience of web applications. In the\u0000diverse ecosystem of web technologies, various protocols like RESTful APIs,\u0000gRPC, WebSockets, and others serve distinct purposes. RESTful APIs are widely\u0000favored for their simplicity and stateless nature, making them ideal for\u0000standard CRUD operations. They offer a straightforward approach to interacting\u0000with resources over HTTP/1.1, providing broad compatibility and ease of\u0000integration across different platforms. However, in scenarios where\u0000applications require high efficiency and real-time communication, gRPC and\u0000WebSockets emerge as powerful alternatives. Each protocol comes with its\u0000strengths and limitations, influencing factors such as ease of implementation,\u0000performance under load, and support for complex data structures. RESTful APIs,\u0000while easy to use and widely supported, may introduce overhead due to their\u0000stateless nature and reliance on multiple HTTP/1.1 requests. In contrast, gRPC\u0000advanced features, while powerful, require a steeper learning curve and more\u0000sophisticated infrastructure. Similarly, WebSockets, while excellent for\u0000real-time applications, require careful management of persistent connections\u0000and security considerations. This paper explores the key considerations in\u0000choosing the right communication protocol, emphasizing the need to align\u0000technical choices with application requirements and user expectations. By\u0000understanding the unique attributes of each protocol, developers can make\u0000informed decisions that enhance the responsiveness and reliability of their web\u0000applications. The choice of protocol can significantly impact the user\u0000experience, scalability, and maintainability of the application, making it a\u0000critical decision in the web development process.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes 探索在工业测试维护流程中整合大型语言模型

arXiv - CS - Software Engineering Pub Date : 2024-09-10 DOI: arxiv-2409.06416

Ludvig Lemner, Linnea Wahlgren, Gregory Gay, Nasser Mohammadiha, Jingxiong Liu, Joakim Wennerberg

{"title":"Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes","authors":"Ludvig Lemner, Linnea Wahlgren, Gregory Gay, Nasser Mohammadiha, Jingxiong Liu, Joakim Wennerberg","doi":"arxiv-2409.06416","DOIUrl":"https://doi.org/arxiv-2409.06416","url":null,"abstract":"Much of the cost and effort required during the software testing process is\u0000invested in performing test maintenance - the addition, removal, or\u0000modification of test cases to keep the test suite in sync with the\u0000system-under-test or to otherwise improve its quality. Tool support could\u0000reduce the cost - and improve the quality - of test maintenance by automating\u0000aspects of the process or by providing guidance and support to developers. In this study, we explore the capabilities and applications of large language\u0000models (LLMs) - complex machine learning models adapted to textual analysis -\u0000to support test maintenance. We conducted a case study at Ericsson AB where we\u0000explored the triggers that indicate the need for test maintenance, the actions\u0000that LLMs can take, and the considerations that must be made when deploying\u0000LLMs in an industrial setting. We also proposed and demonstrated\u0000implementations of two multi-agent architectures that can predict which test\u0000cases require maintenance following a change to the source code. Collectively,\u0000these contributions advance our theoretical and practical understanding of how\u0000LLMs can be deployed to benefit industrial test maintenance processes.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Applying Bandit Algorithm to Fault Localization Techniques 将 Bandit 算法应用于故障定位技术

arXiv - CS - Software Engineering Pub Date : 2024-09-10 DOI: arxiv-2409.06268

Masato Nakao, Kensei Hamamoto, Masateru Tsunoda, Amjed Tahir, Koji Toda, Akito Monden, Keitaro Nakasai, Kenichi Matsumoto

引用次数: 0

Development and Benchmarking of Multilingual Code Clone Detector 多语言代码克隆检测器的开发与基准测试

arXiv - CS - Software Engineering Pub Date : 2024-09-10 DOI: arxiv-2409.06176

Wenqing Zhu, Norihiro Yoshida, Toshihiro Kamiya, Eunjong Choi, Hiroaki Takada

{"title":"Development and Benchmarking of Multilingual Code Clone Detector","authors":"Wenqing Zhu, Norihiro Yoshida, Toshihiro Kamiya, Eunjong Choi, Hiroaki Takada","doi":"arxiv-2409.06176","DOIUrl":"https://doi.org/arxiv-2409.06176","url":null,"abstract":"The diversity of programming languages is growing, making the language\u0000extensibility of code clone detectors crucial. However, this is challenging for\u0000most existing clone detection detectors because the source code handler needs\u0000modifications, which require specialist-level knowledge of the targeted\u0000language and is time-consuming. Multilingual code clone detectors make it\u0000easier to add new language support by providing syntax information of the\u0000target language only. To address the shortcomings of existing multilingual\u0000detectors for language scalability and detection performance, we propose a\u0000multilingual code block extraction method based on ANTLR parser generation, and\u0000implement a multilingual code clone detector (MSCCD), which supports the most\u0000significant number of languages currently available and has the ability to\u0000detect Type-3 code clones. We follow the methodology of previous studies to\u0000evaluate the detection performance of the Java language. Compared to ten\u0000state-of-the-art detectors, MSCCD performs at an average level while it also\u0000supports a significantly larger number of languages. Furthermore, we propose\u0000the first multilingual syntactic code clone evaluation benchmark based on the\u0000CodeNet database. Our results reveal that even when applying the same detection\u0000approach, performance can vary markedly depending on the language of the source\u0000code under investigation. Overall, MSCCD is the most balanced one among the\u0000evaluated tools when considering detection performance and language\u0000extensibility.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data HexaCoder：通过 Oracle 引导的合成训练数据安全生成代码

arXiv - CS - Software Engineering Pub Date : 2024-09-10 DOI: arxiv-2409.06446

Hossein Hajipour, Lea Schönherr, Thorsten Holz, Mario Fritz

{"title":"HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data","authors":"Hossein Hajipour, Lea Schönherr, Thorsten Holz, Mario Fritz","doi":"arxiv-2409.06446","DOIUrl":"https://doi.org/arxiv-2409.06446","url":null,"abstract":"Large language models (LLMs) have shown great potential for automatic code\u0000generation and form the basis for various tools such as GitHub Copilot.\u0000However, recent studies highlight that many LLM-generated code contains serious\u0000security vulnerabilities. While previous work tries to address this by training\u0000models that generate secure code, these attempts remain constrained by limited\u0000access to training data and labor-intensive data preparation. In this paper, we introduce HexaCoder, a novel approach to enhance the\u0000ability of LLMs to generate secure codes by automatically synthesizing secure\u0000codes, which reduces the effort of finding suitable training data. HexaCoder\u0000comprises two key components: an oracle-guided data synthesis pipeline and a\u0000two-step process for secure code generation. The data synthesis pipeline\u0000generates pairs of vulnerable and fixed codes for specific Common Weakness\u0000Enumeration (CWE) types by utilizing a state-of-the-art LLM for repairing\u0000vulnerable code. A security oracle identifies vulnerabilities, and a\u0000state-of-the-art LLM repairs them by extending and/or editing the codes,\u0000creating data pairs for fine-tuning using the Low-Rank Adaptation (LoRA)\u0000method. Each example of our fine-tuning dataset includes the necessary\u0000security-related libraries and code that form the basis of our novel two-step\u0000generation approach. This allows the model to integrate security-relevant\u0000libraries before generating the main code, significantly reducing the number of\u0000generated vulnerable codes by up to 85% compared to the baseline methods. We\u0000perform extensive evaluations on three different benchmarks for four LLMs,\u0000demonstrating that HexaCoder not only improves the security of the generated\u0000code but also maintains a high level of functional correctness.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Empirical Study of the Impact of Test Strategies on Online Optimization for Ensemble-Learning Defect Prediction 测试策略对集合学习缺陷预测在线优化影响的实证研究

arXiv - CS - Software Engineering Pub Date : 2024-09-10 DOI: arxiv-2409.06264

Kensei Hamamoto, Masateru Tsunoda, Amjed Tahir, Kwabena Ebo Bennin, Akito Monden, Koji Toda, Keitaro Nakasai, Kenichi Matsumoto

{"title":"An Empirical Study of the Impact of Test Strategies on Online Optimization for Ensemble-Learning Defect Prediction","authors":"Kensei Hamamoto, Masateru Tsunoda, Amjed Tahir, Kwabena Ebo Bennin, Akito Monden, Koji Toda, Keitaro Nakasai, Kenichi Matsumoto","doi":"arxiv-2409.06264","DOIUrl":"https://doi.org/arxiv-2409.06264","url":null,"abstract":"Ensemble learning methods have been used to enhance the reliability of defect\u0000prediction models. However, there is an inconclusive stability of a single\u0000method attaining the highest accuracy among various software projects. This\u0000work aims to improve the performance of ensemble-learning defect prediction\u0000among such projects by helping select the highest accuracy ensemble methods. We\u0000employ bandit algorithms (BA), an online optimization method, to select the\u0000highest-accuracy ensemble method. Each software module is tested sequentially,\u0000and bandit algorithms utilize the test outcomes of the modules to evaluate the\u0000performance of the ensemble learning methods. The test strategy followed might\u0000impact the testing effort and prediction accuracy when applying online\u0000optimization. Hence, we analyzed the test order's influence on BA's\u0000performance. In our experiment, we used six popular defect prediction datasets,\u0000four ensemble learning methods such as bagging, and three test strategies such\u0000as testing positive-prediction modules first (PF). Our results show that when\u0000BA is applied with PF, the prediction accuracy improved on average, and the\u0000number of found defects increased by 7% on a minimum of five out of six\u0000datasets (although with a slight increase in the testing effort by about 4%\u0000from ordinal ensemble learning). Hence, BA with PF strategy is the most\u0000effective to attain the highest prediction accuracy using ensemble methods on\u0000various projects.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI for Requirements Engineering: A Systematic Literature Review 用于需求工程的生成式人工智能：系统文献综述

arXiv - CS - Software Engineering Pub Date : 2024-09-10 DOI: arxiv-2409.06741

Haowei Cheng, Jati H. Husen, Sien Reeve Peralta, Bowen Jiang, Nobukazu Yoshioka, Naoyasu Ubayashi, Hironori Washizaki

{"title":"Generative AI for Requirements Engineering: A Systematic Literature Review","authors":"Haowei Cheng, Jati H. Husen, Sien Reeve Peralta, Bowen Jiang, Nobukazu Yoshioka, Naoyasu Ubayashi, Hironori Washizaki","doi":"arxiv-2409.06741","DOIUrl":"https://doi.org/arxiv-2409.06741","url":null,"abstract":"Context: Generative AI (GenAI) has emerged as a transformative tool in\u0000software engineering, with requirements engineering (RE) actively exploring its\u0000potential to revolutionize processes and outcomes. The integration of GenAI\u0000into RE presents both promising opportunities and significant challenges that\u0000necessitate systematic analysis and evaluation. Objective: This paper presents\u0000a comprehensive systematic literature review (SLR) analyzing state-of-the-art\u0000applications and innovative proposals leveraging GenAI in RE. It surveys\u0000studies focusing on the utilization of GenAI to enhance RE processes while\u0000identifying key challenges and opportunities in this rapidly evolving field.\u0000Method: A rigorous SLR methodology was used to analyze 27 carefully selected\u0000primary studies in-depth. The review examined research questions pertaining to\u0000the application of GenAI across various RE phases, the models and techniques\u0000used, and the challenges encountered in implementation and adoption. Results:\u0000The most salient findings include i) a predominant focus on the early stages of\u0000RE, particularly the elicitation and analysis of requirements, indicating\u0000potential for expansion into later phases; ii) the dominance of large language\u0000models, especially the GPT series, highlighting the need for diverse AI\u0000approaches; and iii) persistent challenges in domain-specific applications and\u0000the interpretability of AI-generated outputs, underscoring areas requiring\u0000further research and development. Conclusions: The results highlight the\u0000critical need for comprehensive evaluation frameworks, improved human-AI\u0000collaboration models, and thorough consideration of ethical implications in\u0000GenAI-assisted RE. Future research should prioritize extending GenAI\u0000applications across the entire RE lifecycle, enhancing domain-specific\u0000capabilities, and developing strategies for responsible AI integration in RE\u0000practices.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"214 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Think-on-Process: Dynamic Process Generation for Collaborative Development of Multi-Agent System 流程上的思考：多代理系统协作开发的动态流程生成

arXiv - CS - Software Engineering Pub Date : 2024-09-10 DOI: arxiv-2409.06568

Leilei Lin, Yingming Zhou, Wenlong Chen, Chen Qian

{"title":"Think-on-Process: Dynamic Process Generation for Collaborative Development of Multi-Agent System","authors":"Leilei Lin, Yingming Zhou, Wenlong Chen, Chen Qian","doi":"arxiv-2409.06568","DOIUrl":"https://doi.org/arxiv-2409.06568","url":null,"abstract":"Software development is a collaborative endeavor that requires individuals\u0000from different departments to work together in order to collectively develop a\u0000high-quality software system. In this context, people have begun to explore a\u0000method that leverages multi-agent systems based on LLMs to carry out software\u0000development. However, existing research tends to rigidly fix the software\u0000development process in a framework in code form, thus failing to dynamically\u0000adjust the software development process in real-time to meet the more flexible\u0000and variable software environment. In this paper, we propose a dynamic process\u0000generation framework, named ToP (Think-on-Process). The core idea of ToP is to\u0000leverage experiential knowledge (i.e., process models) to guide LLMs in\u0000generating software development processes (i.e., instances). These instances\u0000will guide multi-agent in software development and employ a compiler to provide\u0000feedback on the development outcomes. Subsequently, we utilize heuristic\u0000algorithms to filter the instances and apply process mining algorithms to\u0000derive process model. Finally, the process model will be converted into text,\u0000formatted as prompts, to enhance the ability of LLMs to generate other\u0000instances. Experiments demonstrate that our framework ToP significantly\u0000enhances the dynamic process generation capability of the GPT-3.5 and GPT-4 for\u0000five categories of software development tasks.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JavaVFC: Java Vulnerability Fixing Commits from Open-source Software JavaVFC：开源软件的 Java 漏洞修复承诺

arXiv - CS - Software Engineering Pub Date : 2024-09-09 DOI: arxiv-2409.05576

Tan Bui, Yan Naing Tun, Yiran Cheng, Ivana Clairine Irsan, Ting Zhang, Hong Jin Kang

{"title":"JavaVFC: Java Vulnerability Fixing Commits from Open-source Software","authors":"Tan Bui, Yan Naing Tun, Yiran Cheng, Ivana Clairine Irsan, Ting Zhang, Hong Jin Kang","doi":"arxiv-2409.05576","DOIUrl":"https://doi.org/arxiv-2409.05576","url":null,"abstract":"We present a comprehensive dataset of Java vulnerability-fixing commits\u0000(VFCs) to advance research in Java vulnerability analysis. Our dataset, derived\u0000from thousands of open-source Java projects on GitHub, comprises two variants:\u0000JavaVFC and JavaVFC-extended. The dataset was constructed through a rigorous\u0000process involving heuristic rules and multiple rounds of manual labeling. We\u0000initially used keywords to filter candidate VFCs based on commit messages, then\u0000refined this keyword set through iterative manual labeling. The final labeling\u0000round achieved a precision score of 0.7 among three annotators. We applied the\u0000refined keyword set to 34,321 open-source Java repositories with over 50 GitHub\u0000stars, resulting in JavaVFC with 784 manually verified VFCs and\u0000JavaVFC-extended with 16,837 automatically identified VFCs. Both variants are\u0000presented in a standardized JSONL format for easy access and analysis. This\u0000dataset supports various research endeavors, including VFC identification,\u0000fine-grained vulnerability detection, and automated vulnerability repair. The\u0000JavaVFC and JavaVFC-extended are publicly available at\u0000https://zenodo.org/records/13731781.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

$mathbb{USCD}$: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding $mathbb{USCD}$：通过不确定性感知的选择性对比解码改进 LLM 的代码生成

arXiv - CS - Software Engineering Pub Date : 2024-09-09 DOI: arxiv-2409.05923

Shuai Wang, Liang Ding, Li Shen, Yong Luo, Zheng He, Wei Yu, Dacheng Tao

{"title":"$mathbb{USCD}$: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding","authors":"Shuai Wang, Liang Ding, Li Shen, Yong Luo, Zheng He, Wei Yu, Dacheng Tao","doi":"arxiv-2409.05923","DOIUrl":"https://doi.org/arxiv-2409.05923","url":null,"abstract":"Large language models (LLMs) have shown remarkable capabilities in code\u0000generation. However, the effects of hallucinations (e.g., output noise) make it\u0000particularly challenging for LLMs to generate high-quality code in one pass. In\u0000this work, we propose a simple and effective textbf{u}ncertainty-aware\u0000textbf{s}elective textbf{c}ontrastive textbf{d}ecoding ($mathbb{USCD}$)\u0000mechanism to improve the quality of one-pass code generation in LLMs and reduce\u0000the impact of output noise. To be specific, we first elaborately designed a\u0000negative prompt (namely lame prompt) to output noise by removing input-output\u0000examples from the standard few-shot prompt. Our preliminary study shows that\u0000the Jensen-Shannon divergence (JS divergence) between token distribution\u0000uncertainty and the output noise is relatively low (approximately $0.25$),\u0000indicating their high relevance. Then, we selectively eliminate output noise\u0000induced by lame prompts based on the uncertainty of the prediction distribution\u0000from the standard prompt. Notably, our proposed plug-and-play mechanism is an\u0000inference-only method, enjoying appealing flexibility. Extensive experiments on\u0000widely used benchmarks, e.g., HumanEval, MBPP, and MultiPL-E, upon several LLMs\u0000(i.e., Inocder-6b, CodeLlama-7b, WizardCoder-15b, StarCoder, and Llama2-7b),\u0000demonstrate that our proposed USCD significantly improves one-pass code\u0000generation, with an average textit{pass@$1$} scores increase of 16.59%. We\u0000will release code and data on GitHub.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0