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检索条件"任意字段=6th International Conference on Generative Programming and Component Engineering"

共 292 条记录，以下是1-10 订阅

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Leveraging Large Language Models for Python Unit Test 6

Leveraging Large Language Models for Python Unit Test

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6th IEEE international conference on Artificial Intelligence Testing (AITest) as a part of the IEEE international Congress on Intelligence and Service-Oriented Systems engineering (CISOSE)

作者： Jiri, Medlen Emese, Bari Medlen, Patrick LIGS Univ Honolulu HI 96813 USA Quant Univ Washington DC USA

ISBN: (纸本)9798350365054

generative Artificial Intelligence is becoming an integral and enduring part of our lives, growing more powerful with each passing day. this paper explores Large Language Models and their application in text generation, specifically examining their potential to assist software quality assurance engineers in their daily tasks. Our focus is on the generation of unit tests as a critical component of software development. the research question is simple: Can generative AI generate comprehensive unit tests? We started with Python and a very simple use case, and if Gen AI is successful, we will continue with complex tasks. Current literature focuses on success, but we are interested in failures as well. How many test cases are missing?

关键词： generative Artificial Intelligence Software Quality Assurance Unit Test Python

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Analyzing the Role of generative AI in Fostering Self-directed Learning through Structured Prompt engineering 20th

Analyzing the Role of Generative AI in Fostering Self-direct...

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20th international conference on Intelligent Tutoring Systems (ITS)

作者： Garg, Ashish Rajendran, Ramkumar Indian Inst Technol IDP Educ Technol Mumbai Maharashtra India

ISBN: (纸本)9783031630279;9783031630286

this study explores the use of generative AI, particularly large language models such as ChatGPT, in promoting self-directed learning among beginners in programming and data analysis, in the study structured prompts were employed as a key tool to enhance educational engagement and skill acquisition. To study the impact, engineering students participated in a controlled environment where they utilized these prompts in conjunction with generative AI to tackle programming-based data analysis tasks independently. We measured the impact of this method by comparing pre-test and post-test scores, which showed a significant improvement, indicating its effectiveness. Moreover, 45% of novice participants completed all assigned tasks. We also conducted semi-structured interviews and analyzed participant responses to understand the role of prompt engineering in self-directed learning. the analysis revealed that structured prompts and generative AI motivate students and empower them to learn independently.

关键词： Chatgpt Prompt engineering Self-directed learning Data analysis

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6th international School on engineering Trustworthy Software Systems, SETSS 2024

6th International School on Engineering Trustworthy Software...

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6th international School on engineering Trustworthy Software Systems, SETSS 2024

the proceedings contain 7 papers. the special focus in this conference is on engineering Trustworthy Software Systems. the topics include: From Logic to60;programming;digital Twin Tutorial: the Incubator Case Study...

ISBN: (纸本)9789819646555

the proceedings contain 7 papers. the special focus in this conference is on engineering Trustworthy Software Systems. the topics include: From Logic to programming;digital Twin Tutorial: the Incubator Case Study;AI components for High Integrity, Safety-Critical Human-Cyber-Physical Systems: A Challenge for Formal Methods;Testing and Design of Uniform CNF Samplers: A Virtuous Cycle Enabled by Distribution Testing;software engineering Experiences of an Optimist;Automating component-Based Embedded Software Construction via Formal Synthesis and LLMs.

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Application to Enhance the programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

this proposal outlines the development of a comprehensive educational platform aimed at bridging the Information Technology knowledge gap among Tamil-speaking students in Sri Lanka. the platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. the application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. this model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. this component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information Technology skills. the second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. the platform also integrates the OpenAI Application programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. the platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Multi-Vehicle Charging Infrastructure and Routing Using Mixed-Integer programming and Pareto Ranking-Based Genetic Algorithms 6

Optimizing Multi-Vehicle Charging Infrastructure and Routing...

引用

6th IEEE international conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Xu, Qiheng Chang'an University Automotive Engineering Xi'an China

ISBN: (纸本)9798350374315

this research addresses the optimization of routes and the selection of locations for multiple electric logistics vehicle charging stations. Electric logistics vehicles, as a key component of new energy vehicles, offer significant environmental benefits such as zero emissions and high energy efficiency, making them ideal for urban logistics. However, efficient operation depends on the strategic placement of charging stations and effective route planning. this research introduces a mixed-integer programming model incorporating multi-vehicle configuration and genetic algorithms to solve the charging station location and path optimization problems. the model aims to minimize transportation costs, charging costs, time window penalties, environmental costs, and charging station construction costs. By factoring in battery capacity and time window constraints, the model ensures vehicles complete delivery tasks efficiently. the proposed Pareto ranking-based GA shows robust performance in generating high-quality solutions, as demonstrated by small, medium and large-scale examples. Sensitivity analysis confirms the model's practicality and reliability, showing the total delivery cost stabilizes as battery capacity increases. this study contributes to enhancing the operational efficiency and adoption of electric logistics vehicles through optimized charging infrastructure and route planning. © 2024 IEEE.

关键词： Charging stations

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Discriminant Model Exploration for Classification Stingless Honey Fluorescence Data Spectra with Principal component Analysis (PCA) 6

Discriminant Model Exploration for Classification Stingless ...

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6th international conference on Computer and Informatics engineering, IC2IE 2023

作者： Maulana, Hata Purwanto, Yohanes Aris Wijaya, Sony Hartono Sukoco, Heru Suhandy, Diding Ipb University Department of Computer Science Bogor Indonesia Ipb University Department of Agricultural Engineering and Biosystems Bogor Indonesia Lampung State University Department of Agricultural Engineering Lampung Indonesia

ISBN: (纸本)9798350345162

Honey is an agricultural product that is very easily polluted. One type of honey-producing bee is the stingless bee, where this type of bee has its own uniqueness and quite high selling value. the weakness of honey from this type of bee is that it has a high water content, so it is quite susceptible to damage in terms of quality. therefore, in order to survive in the market, there are treatments such as thickening and adding mixed products to the honey. Some sample data were obtained from the location of this type of honey cultivator, then fluorescence-based spectrum data was collected with certain test scenarios. Discriman model analysis with principal component analysis is a tool to see the distribution of spectral data in this case. Principal component (PC) 1 with a value of 63% and PC 2 only 36% is the basis for consideration for proposing a generative model in processing spectral data that has a higher percentage of information values included in the dataset. © 2023 IEEE.

关键词： discriminant models honey PCA spectra data stingless

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Analyse and Evaluate Quixbugs with Open AI Codex and Powering Next Generation Application 6

Analyse and Evaluate Quixbugs with Open AI Codex and Powerin...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Tyagi, Deepanshu Tanwar, Sarvesh Mittal, Neetu Badotra, Sumit Amity Institute of Information Technology Amity University Uttar Pradesh Noida India School of Computer Science and Engineering Bennett University Uttar Pradesh Noida India

ISBN: (纸本)9798350304480

the paper investigates the ordinary language age limits of massive language models, with applications to the development of two types of learning tools commonly found in programming courses. We construct programming workouts and code explanations, reviewing these abstractly and statistically, using the Open AI Codex as the massive language model. Our findings indicate that the majority of the content so given is both unique and sensible, and that it is occasionally ready to use without any guarantees. While rehearsing, we discovered that it is astonishingly simple to affect both the programming ideas and the practical subjects they include simply by offering expressions of commitment to the model. Findings indicate massively generative computer intelligence models have fundamental value. © 2023 IEEE.

关键词： Application Codex GPT-3 Intelligence Java Open AI Security

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Towards Understanding Rust in the Era of AI for Science at an Ecosystem Scale 6

Towards Understanding Rust in the Era of AI for Science at a...

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6th international conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Xu, Bo Hefei National Research Center Institute of Advanced Technology University of Science and Technology of China Physical Sciences at the Microscale New Cornerstone Science Laboratory Department of Chemistry Hefei230026 China

ISBN: (纸本)9798350353174

Rust is an emerging programming language that provides both memory and thread safety like Java, and runtime efficiency like C/C++, by introducing a number of novel language features such as ownership, borrowing, explicit lifetime, and unsafe. these novel features make Rust a promising safe system language to build infrastructures in AI for science, where both security and performance are indispensable. However, there are no prior empirical studies of Rust language feature usage at the ecosystem scale, and such a study is important not only for guiding future language evolution but also for identifying research opportunities and challenges. In this paper, to fill the gap, we present the first comprehensive empirical study of Rust language feature usage at the ecosystem scale, by analyzing all registered 9,683 science-related crates in the Rust community's crate registry, with a novel tool RInsight we created. the empirical results give important insights, such as: 1) the dominated language features in all crates are functional ones (66.67%), which presents evidence that Rust is essentially a functional language pragmatically, but does NOT support the claim that 'Rust is a safe dialect of C';and 2) the unsafe feature is not used at an as high ratio as the prior study reported and is continuously dropping since 2018. We present the implications of these empirical findings and future research challenges to promote future research. © 2024 IEEE.

关键词： component empirical study Rust secure programming for science

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the Cost of Simplicity: Understanding Datacenter Scheduler programming Abstractions 24

The Cost of Simplicity: Understanding Datacenter Scheduler P...

引用

15th ACM/SPEC international conference on Performance engineering (ICPE)

作者： Lasa, Aratz Manterola Talluri, Sacheendra De Matteis, Tiziano Iosup, Alexandru WarpStream Labs Chicago IL 60612 USA Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400704444

Schedulers are a crucial component in datacenter resource management. Each scheduler offers different capabilities, and users use them through their APIs. However, there is no clear understanding of what programming abstractions they offer, nor why they offer some and not others. Consequently, it is difficult to understand their differences and the performance costs imposed by their APIs. In this work, we study the programming abstractions offered by industrial schedulers, their shortcomings, and their related performance costs. We propose a general reference architecture for scheduler programming abstractions. Specifically, we analyze the programming abstractions of five popular industrial schedulers, understand the differences in their APIs, and identify the missing abstractions. Finally, we carry out exemplary experiments using trace-driven simulation demonstrating that an API extension, such as container migration, can improve total execution time per task by 81%, highlighting how schedulers sacrifice performance by implementing simpler programming abstractions. All the relevant software and data artifacts are publicly available at https://***/atlarge- research/quantifying- api- design.

关键词： cloud scheduler API design performance

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Merge Conflict Resolution: Classification or Generation? 38

Merge Conflict Resolution: Classification or Generation?

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38th IEEE/ACM international conference on Automated Software engineering (ASE)

作者： Dong, Jinhao Zhu, Qihao Sun, Zeyu Lou, Yiling Hao, Dan Peking Univ Sch Comp Sci MoE Key Lab High Confidence Software Technol Peking U Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9798350329964

Collaborative development is critical to improve the productivity. Multiple contributors work simultaneously on the same project and might make changes to the same code locations. this can cause conflicts and require manual intervention from developers to resolve them. To alleviate the human efforts of manual conflict resolution, researchers have proposed various automatic techniques. More recently, deep learning models have been adopted to solve this problem and achieved state-of-the-art performance. However, these techniques leverage classification to combine the existing elements of input. the classification-based models cannot generate new tokens or produce flexible combinations, and have a wrong hypothesis that fine-grained conflicts of one single coarse-grained conflict are independent. In this work, we propose to generate the resolutions of merge conflicts from a totally new perspective, that is, generation, and we present a conflict resolution technique, MergeGen. First, we design a structural and fine-grained conflict-aware representation for the merge conflicts. then, we propose to leverage an encoder-decoder-based generative model to process the designed conflict representation and generate the resolutions auto-regressively. We further perform a comprehensive study to evaluate the effectiveness of MergeGen. the quantitative results show that MergeGen outperforms the state-of-the-art (SOTA) techniques from both precision and accuracy. Our evaluation on multiple programming languages verifies the good generalization ability of MergeGen. In addition, the ablation study shows that the major component of our technique makes a positive contribution to the performance of MergeGen, and the granularity analysis reveals the high tolerance of MergeGen to coarse-grained conflicts. Moreover, the analysis on generating new tokens further proves the advance of generative models.

关键词： Merge Conflict Resolution generative Models Conflict Representation

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