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检索条件"主题词=JAVA programming language"

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VulnSlicer: A java Vulnerability Analysis Tool Based on Vulnerability Slice 10

VulnSlicer: A Java Vulnerability Analysis Tool Based on Vuln...

引用

10th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, DependSys 2024

作者： Liao, Chaohao Zhang, Shihe Liu, Renhua University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798331540500

Due to its exceptional performance and memory security, java is extensively employed in enterprise applications, thereby rendering any vulnerability in java applications capable of causing significant repercussions. In recent years, due to the frequent occurrence of network attacks, new attack methods continue to emerge while software systems become increasingly intricate and unstable under the weight of historical burdens. Consequently, static analysis techniques are imperative for identifying security risks in code during the coding phase. The prevailing approach in static analysis involves transforming code into a standardized intermediate representation and conducting vulnerability detection, without code execution. However, this methodology often yields detection results accompanied by a considerable false positive rate. We propose VulnSlicer, a tool based on graph similarity matching and vulnerability subgraph slice technology. This tool not only enables efficient vulnerability retrieval but also effectively reduces the false positive rate by leveraging the slice mechanism to save historical vulnerabilities. Additionally, it facilitates rapid matching of accumulated vulnerabilities in the database and further mitigates false positives through the introduction of a keyframe mechanism. In the experiment, we conducted a comparative analysis of several existing static detection tools to assess their effectiveness in detecting vulnerabilities within the same codebase. Experiments show that VulnSlicer has decent accuracy and lower false positive rate. © 2024 IEEE.

关键词： java programming language

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Developing a Robust Rental Price Prediction System: Insights from Linear Regression, Decision Trees, and Random Forest

Developing a Robust Rental Price Prediction System: Insights...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Hussain, Mohammed Javed Krishna, P V Satwik Reddy, J Nandan Krishna, J.S. Siva Rama Reshikesh Reddy, Puttamreddy Murali, Saritha Vellore India

ISBN: (纸本)9798331543617

Estimation of the rent rate is an important task in the entire real estate industry and can have a heavy consequence for landlords and future tenants. This paper presents a predictive modeling in rental pricing, using machine learning approaches in the java programming paradigm to build a strong system that could forecast rental values given a set of several property characteristics. Three algorithms were therefore used in this study: Linear Regression, Decision Tree, and Random Forest. The Linear Regression model assumes that there is a basic linear relationship in the middle of the input variables and rental pricing. In contrast, the Decision Tree uses a nonlinear methodology because it segments the data into various decision paths, therefore enhancing its interpretability. The Random Forest approach uses ensemble learning by combining multiple decision trees in order to enhance accuracy and robustness of the prediction. This is accomplished through an integrated web-based platform where these models were integrated into the platform, and a user could insert specific characteristics of the property and would return a set of predicted rental prices. The current study applies these algorithms to a real dataset in order to demonstrate pragmatic usage of machine learning in real estate. Detailed examinations of all three algorithms each have their individual advantages, which give valuable understanding of their efficacy in various prediction situations, hence the creation of a comprehensive instrument for estimating rental prices. © 2024 IEEE.

关键词： java programming language

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A Flexible and Synthetic Transplant Kidney Exchange Problem (FASTKEP) Data Generator

A Flexible and Synthetic Transplant Kidney Exchange Problem ...

引用

IISE Annual Conference and Expo 2024

作者： Nau, Calvin Sankaran, Prashant McConky, Katie Sudit, Moises Khazaelpour, Payam Velasquez, Alvaro Rochester Institute of Technology RochesterNY United States University of Buffalo SUNY BuffaloNY United States Defense Advance Research Projects Agency ArlingtonVA United States

ISBN: (纸本)9781713877851

The Kidney Exchange Problem (KEP) optimizes a pool of incompatible patient-donor pairs and non-directed donors to determine the optimal cycles and chains of length-N that maximize transplants. A direct impact of KEP optimization is an improved quality of life for transplant recipients at a lower cost to the healthcare system (as opposed to dialysis treatments). To improve this process, synthetic data is commonly used to test and develop KEP algorithms. The canonical KEP data generation process is referred to as the Saidman Generator. The generator utilizes attributes of the donors and recipients that determine compatibility, such as blood type and protein compatibility, alongside known data distributions, including the percentage of different blood types comprising an exchange, to create datasets that aim to mirror the real world. Prior published java implementations of this generator exist. However, in this work, an open-source data generation package implemented in Python is proposed for the KEP which mirrors the Saidman generator’s output. The proposed implementation is compared to a common java implementation for scalability and efficiency. This generator allows for future extensions that integrate additional recipient and donor features that produce data better matching real-world exchanges. The result is an open-source Python implementation of a KEP data generator. The efficient creation of directed exchange datasets enables future work, including the use of the presented data generation process by ML/AI researchers in application to large-scale machine learning model training. © IISE Annual Conference and Expo *** rights reserved.

关键词： java programming language

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Visual Analysis of Learning Procrastination Research based on Artificial Intelligence Weight Distribution Module 4

Visual Analysis of Learning Procrastination Research based o...

引用

4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Zhang, Yinyin Zhang, Yuqing Institute of Psychology CAS Research Center for Adolescent Personality and Health Beijing100101 China University of Chinese Academy of Sciences Department of Psychology Beijing100049 China

ISBN: (纸本)9798350354560

In this paper, computer engineering and java language are used for numerical simulation. With the support of artificial intelligence model, the classification model with recognition accuracy greater than 90% and measurement error less than 5% is selected by combining the high-speed computing power of computer with classification algorithm. Through the visual analysis of academic procrastination research by CiteSpace, we can understand the current situation and development trend of academic procrastination research at home and abroad. java software was used to analyze the number of publications, authors and keywords on academic procrastination in CNKI and Web of Science databases. In this paper, a good classification effect is obtained in the application of text statement classification. © 2024 IEEE.

关键词： java programming language

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Digital course scheduling system for data-driven industrial worker training based on 5G enabling environment

Digital course scheduling system for data-driven industrial ...

引用

2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Chen, Yuzhong Zhong, Yue Guangdong Construction Polytechnic Guangdong511500 China

ISBN: (纸本)9798331520816

5G wisdom on the basis of digital campus, campus under the effective use of 5G technology of Internet of things, cloud computing, big data and other advanced information engineering to carry out a full range of campus construction, promoting the scientific decision-making, build open teaching environment, good for all the teachers and students to provide more convenient and comfortable place to live, work, study[1]. This article mainly around college students online course management system implementation technology selection, system requirements, database design and so on carries on the analysis, thus the college students' course selection management system based on B/S structure, B/S architecture to implement development, convenient maintenance, and to implement low cost price, so as to achieve high efficiency low cost development, the main functional modules of the system are: Student and teacher information management, user rights control, course management, classroom management, class management, course selection management, class withdrawal, course selection statistics management, students, teachers and school administrators only need to click on the browser to access the system can carry out the corresponding course selection and system management and maintenance, java programming language and popular frameworks such as Spring Boot, Bootstrap and some Ajax technologies are selected to achieve the smoothness of data interaction at the front and back end of the system. With the advent of the Internet era, the birth of Internet +[2], which can be added to a large number, gradually adds every bit of life, there is no doubt that education has always been a hot issue in the world. © 2024 IEEE.

关键词： java programming language

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Migrating Unit Tests Across java Applications 24

Migrating Unit Tests Across Java Applications

引用

24th IEEE International Conference on Source Code Analysis and Manipulation, SCAM 2024

作者： Jha, Ajay Kumar Nadi, Sarah North Dakota State University Fargo United States New York University Abu Dhabi and University of Alberta Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331528508

Writing effective unit tests is often tedious, difficult, and time-consuming. Test recommendation techniques facilitate this process by recommending existing manually written tests from other similar systems for developers to reuse. However, developers still have to put non-Trivial effort into modifying the recommended tests. For example, they have to understand vari-ous code elements in the recommended tests to accurately replace them with semantically similar code elements from the target system. In this paper, we propose JTESTMIGRATOR, a technique to automatically migrate unit tests between semantically similar methods across applications. Given a source and a target method with similar functionality across applications, where the source method has some unit tests, JTESTMIGRATOR migrates unit tests by transforming the test code. JTESTMIGRATOR uses semantic similarity and type compatibility of code elements in the source and target systems to transform test code. We implement JTESTMIGRATOR to migrate JUnit tests and evaluate it on 104 tests for 42 methods across 5 popular libraries. JTESTMIGRATOR successfully migrates 76 (73%) of the tests for 32 (76%) of the methods across the 5 libraries. 10 (13%) of the successfully migrated tests increase the code coverage of 4 target methods. © 2024 IEEE.

关键词： java programming language

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Human-AI Co-Creation of Worked Examples for programming Classes

Human-AI Co-Creation of Worked Examples for Programming Clas...

引用

2024 ACM International Conference on Intelligent User Interfaces Workshops, IUI-WS 2024

作者： Hassany, Mohammad Brusilovsky, Peter Ke, Jiaze Akhuseyinoglu, Kamil Narayanan, Arun Balajiee Lekshmi University of Pittsburgh PittsburghPA15260 United States Carnegie Mellon University PittsburghPA15213 United States

Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for java programming. We introduce an authoring system for creating java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary. We also present a study that assesses the quality of explanations created with this approach. © 2024 Copyright for this paper by its authors.

关键词： java programming language

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The Fault in our Stars: Quality Assessment of Code Generation Benchmarks 24

The Fault in our Stars: Quality Assessment of Code Generatio...

引用

24th IEEE International Conference on Source Code Analysis and Manipulation, SCAM 2024

作者： Siddiq, Mohammed Latif Dristi, Simantika Saha, Joy Santos, Joanna C. S. University of Notre Dame Notre DameIN United States University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9798331528508

Large language Models (LLMs) are gaining popularity among software engineers. A crucial aspect of developing effective code generation LLMs is to evaluate these models using a robust benchmark. Evaluation benchmarks with quality issues can provide a false sense of performance. In this work, we conduct the first-of-its-kind study of the quality of prompts within benchmarks used to compare the performance of different code generation models. To conduct this study, we analyzed 3,566 prompts from 9 code generation benchmarks to identify quality issues in them. We also investigated whether fixing the identified quality issues in the benchmarks' prompts affects a model's performance. We also studied memorization issues of the evaluation dataset, which can put into question a benchmark's trustworthiness. We found that code generation evaluation benchmarks mainly focused on Python and coding exercises and had very limited contextual dependencies to challenge the model. These datasets and the developers' prompts suffer from quality issues like spelling and grammatical errors, unclear sentences to express developers' intent, and not using proper documentation style. Fixing all these issues in the benchmarks can lead to a better performance for Python code generation, but not a significant improvement was observed for java code generation. We also found evidence that GPT-3.5-Turbo and CodeGen-2.5 models may have data contamination issues. © 2024 IEEE.

关键词： java programming language

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javaLLM: A Fine-Tuned LLM for java programming Education 8

JavaLLM: A Fine-Tuned LLM for Java Programming Education

引用

8th International Symposium on Computer Science and Intelligent Control, ISCSIC 2024

作者： Zhang, Jingying Liu, Kang Hunan Urban Professional College School of Information Science and Engineering Changsha China

ISBN: (纸本)9798350380286

The integration of Large language Models (LLMs) into education marks a significant advancement toward personalized and adaptive learning environments, particularly in programming education. Addressing the limitations of existing LLMs in specialized domains like java programming, this paper introduces javaLLM - a model specifically tailored for java programming education. Built upon a robust codeLLM and fine-tuned using extensive, high-quality java-focused datasets, javaLLM demonstrates superior performance in code generation and java-specific question answering. Through rigorous evaluation and iterative refinement, javaLLM facilitates a transformative classroom experience, enhancing the quality of teaching and enabling a personalized learning journey for students in java programming courses. This innovation paves the way for smarter, more tailored educational approaches, leveraging AI's generative capabilities to meet the evolving demands of modern education. © 2024 IEEE.

关键词： java programming language

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On the Impact of PowerCap in Haskell, java, and Python 3

On the Impact of PowerCap in Haskell, Java, and Python

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3rd International Workshop on Resource AWareness of Systems and Society, RAW 2024

作者： Maia, Luís Sá, Marta Ferreira, Inês Cunha, Simão Silva, Luís Azevedo, Paulo Saraiva, João Department of Informatics University of Minho Portugal HasLab INESC TEC Portugal

Historically, programming language performance focused on fast execution times. With the advent of cloud and edge computing, and the significant energy consumption of large data centers, energy efficiency has become a critical concern both for computer manufacturers and software developers. Despite the considerable efforts of the green software community in developing techniques and tools for analysing and optimising software energy consumption, there has been limited research on how imposing hardware-level energy constraints affects software energy efficiency. Moreover, prior research has demonstrated that the choice of programming language can significantly impact a program’s energy efficiency. This paper investigates the impact of CPU power capping on the energy consumption and execution time of programs written in Haskell, java, and Python. Our preliminary results analysing well-established benchmarks indicate that while power capping does reduce energy consumption across all benchmarks, it also substantially increases execution time. These findings highlight the trade-offs between energy efficiency and runtime performance, offering insights for optimising software under energy constraints. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： java programming language

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