arXiv - CS - Software Engineering最新文献

Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review 软件工程中机器人和对话式代理的动机、挑战、最佳实践和优势：多语种文献综述

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.11864

Stefano Lambiase, Gemma Catolino, Fabio Palomba, Filomena Ferrucci

{"title":"Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review","authors":"Stefano Lambiase, Gemma Catolino, Fabio Palomba, Filomena Ferrucci","doi":"arxiv-2409.11864","DOIUrl":"https://doi.org/arxiv-2409.11864","url":null,"abstract":"Bots are software systems designed to support users by automating a specific\u0000process, task, or activity. When such systems implement a conversational\u0000component to interact with the users, they are also known as conversational\u0000agents. Bots, particularly in their conversation-oriented version and\u0000AI-powered, have seen their adoption increase over time for software\u0000development and engineering purposes. Despite their exciting potential,\u0000ulteriorly enhanced by the advent of Generative AI and Large Language Models,\u0000bots still need to be improved to develop and integrate into the development\u0000cycle since practitioners report that bots add additional challenges that may\u0000worsen rather than improve. In this work, we aim to provide a taxonomy for\u0000characterizing bots, as well as a series of challenges for their adoption for\u0000Software Engineering associated with potential mitigation strategies. To reach\u0000our objectives, we conducted a multivocal literature review, reviewing both\u0000research and practitioner's literature. Through such an approach, we hope to\u0000contribute to both researchers and practitioners by providing first, a series\u0000of future research routes to follow, second, a list of strategies to adopt for\u0000improving the use of bots for software engineering purposes, and third, enforce\u0000a technology and knowledge transfer from the research field to the\u0000practitioners one, that is one of the primary goal of multivocal literature\u0000reviews.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261261","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

Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization 协作式代码生成模式的前景与危险：平衡效率与记忆

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.12020

Zhi Chen, Lingxiao Jiang

{"title":"Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization","authors":"Zhi Chen, Lingxiao Jiang","doi":"arxiv-2409.12020","DOIUrl":"https://doi.org/arxiv-2409.12020","url":null,"abstract":"In the rapidly evolving field of machine learning, training models with\u0000datasets from various locations and organizations presents significant\u0000challenges due to privacy and legal concerns. The exploration of effective\u0000collaborative training settings capable of leveraging valuable knowledge from\u0000distributed and isolated datasets is increasingly crucial. This study\u0000investigates key factors that impact the effectiveness of collaborative\u0000training methods in code next-token prediction, as well as the correctness and\u0000utility of the generated code, demonstrating the promise of such methods.\u0000Additionally, we evaluate the memorization of different participant training\u0000data across various collaborative training settings, including centralized,\u0000federated, and incremental training, highlighting their potential risks in\u0000leaking data. Our findings indicate that the size and diversity of code\u0000datasets are pivotal factors influencing the success of collaboratively trained\u0000code models. We show that federated learning achieves competitive performance\u0000compared to centralized training while offering better data protection, as\u0000evidenced by lower memorization ratios in the generated code. However,\u0000federated learning can still produce verbatim code snippets from hidden\u0000training data, potentially violating privacy or copyright. Our study further\u0000explores effectiveness and memorization patterns in incremental learning,\u0000emphasizing the sequence in which individual participant datasets are\u0000introduced. We also identify cross-organizational clones as a prevalent\u0000challenge in both centralized and federated learning scenarios. Our findings\u0000highlight the persistent risk of data leakage during inference, even when\u0000training data remains unseen. We conclude with recommendations for\u0000practitioners and researchers to optimize multisource datasets, propelling\u0000cross-organizational collaboration forward.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261199","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

Prototypical Leadership in Agile Software Development 敏捷软件开发中的原型领导力

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.11685

Jina Dawood, Lucas Gren

引用次数: 0

Shannon Entropy is better Feature than Category and Sentiment in User Feedback Processing 在用户反馈处理中，香农熵是比类别和情感更好的特征

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.12012

Andres Rojas Paredes, Brenda Mareco

引用次数: 0

From Group Psychology to Software Engineering Research to Automotive R&D: Measuring Team Development at Volvo Cars 从群体心理学到软件工程研究再到汽车研发：沃尔沃汽车公司团队发展的衡量标准

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.11778

Lucas Gren, Christian Jacobsson

引用次数: 0

Model-Checking the Implementation of Consent 对同意的执行情况进行示范检查

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.11803

Raúl Pardo, Daniel Le Métayer

引用次数: 0

A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems 深度学习系统中自我承认的技术债务分类标准

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.11826

Federica Pepe, Fiorella Zampetti, Antonio Mastropaolo, Gabriele Bavota, Massimiliano Di Penta

{"title":"A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems","authors":"Federica Pepe, Fiorella Zampetti, Antonio Mastropaolo, Gabriele Bavota, Massimiliano Di Penta","doi":"arxiv-2409.11826","DOIUrl":"https://doi.org/arxiv-2409.11826","url":null,"abstract":"The development of Machine Learning (ML)- and, more recently, of Deep\u0000Learning (DL)-intensive systems requires suitable choices, e.g., in terms of\u0000technology, algorithms, and hyper-parameters. Such choices depend on\u0000developers' experience, as well as on proper experimentation. Due to limited\u0000time availability, developers may adopt suboptimal, sometimes temporary\u0000choices, leading to a technical debt (TD) specifically related to the ML code.\u0000This paper empirically analyzes the presence of Self-Admitted Technical Debt\u0000(SATD) in DL systems. After selecting 100 open-source Python projects using\u0000popular DL frameworks, we identified SATD from their source comments and\u0000created a stratified sample of 443 SATD to analyze manually. We derived a\u0000taxonomy of DL-specific SATD through open coding, featuring seven categories\u0000and 41 leaves. The identified SATD categories pertain to different aspects of\u0000DL models, some of which are technological (e.g., due to hardware or libraries)\u0000and some related to suboptimal choices in the DL process, model usage, or\u0000configuration. Our findings indicate that DL-specific SATD differs from DL bugs\u0000found in previous studies, as it typically pertains to suboptimal solutions\u0000rather than functional (eg blocking) problems. Last but not least, we found\u0000that state-of-the-art static analysis tools do not help developers avoid such\u0000problems, and therefore, specific support is needed to cope with DL-specific\u0000SATD.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261262","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

Investigating team maturity in an agile automotive reorganization 调查敏捷汽车重组中的团队成熟度

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.11781

Lucas Gren, Niclas Pettersson

引用次数: 0

Bridging Design and Development with Automated Declarative UI Code Generation 自动生成声明式用户界面代码，架起设计与开发的桥梁

arXiv - CS - Software Engineering Pub Date : 2024-09-18 DOI: arxiv-2409.11667

Ting Zhou, Yanjie Zhao, Xinyi Hou, Xiaoyu Sun, Kai Chen, Haoyu Wang

{"title":"Bridging Design and Development with Automated Declarative UI Code Generation","authors":"Ting Zhou, Yanjie Zhao, Xinyi Hou, Xiaoyu Sun, Kai Chen, Haoyu Wang","doi":"arxiv-2409.11667","DOIUrl":"https://doi.org/arxiv-2409.11667","url":null,"abstract":"Declarative UI frameworks have gained widespread adoption in mobile app\u0000development, offering benefits such as improved code readability and easier\u0000maintenance. Despite these advantages, the process of translating UI designs\u0000into functional code remains challenging and time-consuming. Recent\u0000advancements in multimodal large language models (MLLMs) have shown promise in\u0000directly generating mobile app code from user interface (UI) designs. However,\u0000the direct application of MLLMs to this task is limited by challenges in\u0000accurately recognizing UI components and comprehensively capturing interaction\u0000logic. To address these challenges, we propose DeclarUI, an automated approach that\u0000synergizes computer vision (CV), MLLMs, and iterative compiler-driven\u0000optimization to generate and refine declarative UI code from designs. DeclarUI\u0000enhances visual fidelity, functional completeness, and code quality through\u0000precise component segmentation, Page Transition Graphs (PTGs) for modeling\u0000complex inter-page relationships, and iterative optimization. In our\u0000evaluation, DeclarUI outperforms baselines on React Native, a widely adopted\u0000declarative UI framework, achieving a 96.8% PTG coverage rate and a 98%\u0000compilation success rate. Notably, DeclarUI demonstrates significant\u0000improvements over state-of-the-art MLLMs, with a 123% increase in PTG coverage\u0000rate, up to 55% enhancement in visual similarity scores, and a 29% boost in\u0000compilation success rate. We further demonstrate DeclarUI's generalizability\u0000through successful applications to Flutter and ArkUI frameworks.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261266","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

AutoSpec: Automated Generation of Neural Network Specifications AutoSpec：自动生成神经网络规范

arXiv - CS - Software Engineering Pub Date : 2024-09-17 DOI: arxiv-2409.10897

Shuowei Jin, Francis Y. Yan, Cheng Tan, Anuj Kalia, Xenofon Foukas, Z. Morley Mao

引用次数: 0