GenProgJS: a Baseline System for Test-based Automated Repair of JavaScript Programs

IF 6.5 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING IEEE Transactions on Software Engineering Pub Date : 2024-11-21 DOI:10.1109/tse.2024.3497798
Viktor Csuvik, Dániel Horváth, Márk Lajkó, László Vidács
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GenProgJS:基于测试的 JavaScript 程序自动修复基准系统
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来源期刊
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering 工程技术-工程:电子与电气
CiteScore
9.70
自引率
10.80%
发文量
724
审稿时长
6 months
期刊介绍: IEEE Transactions on Software Engineering seeks contributions comprising well-defined theoretical results and empirical studies with potential impacts on software construction, analysis, or management. The scope of this Transactions extends from fundamental mechanisms to the development of principles and their application in specific environments. Specific topic areas include: a) Development and maintenance methods and models: Techniques and principles for specifying, designing, and implementing software systems, encompassing notations and process models. b) Assessment methods: Software tests, validation, reliability models, test and diagnosis procedures, software redundancy, design for error control, and measurements and evaluation of process and product aspects. c) Software project management: Productivity factors, cost models, schedule and organizational issues, and standards. d) Tools and environments: Specific tools, integrated tool environments, associated architectures, databases, and parallel and distributed processing issues. e) System issues: Hardware-software trade-offs. f) State-of-the-art surveys: Syntheses and comprehensive reviews of the historical development within specific areas of interest.
期刊最新文献
GenProgJS: a Baseline System for Test-based Automated Repair of JavaScript Programs On Inter-dataset Code Duplication and Data Leakage in Large Language Models Line-Level Defect Prediction by Capturing Code Contexts with Graph Convolutional Networks Does Treatment Adherence Impact Experiment Results in TDD? Scoping Software Engineering for AI: The TSE Perspective
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