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International Journal of Software Engineering and Knowledge Engineering最新文献

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Software Industry Perception of Technical Debt and its Management 软件行业对技术债务及其管理的看法
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2023-10-06 DOI: 10.1142/s0218194023500602
Cecilia Apa, Martin Solari, Diego Vallespir, G. Travassos
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引用次数: 0
CodeGen-Search: A Code Generation Model Incorporating Similar Sample Information 代码搜索:包含相似样本信息的代码生成模型
4区 计算机科学 Q3 Computer Science Pub Date : 2023-09-29 DOI: 10.1142/s0218194023500584
HongWei Li, JiangLing Kuang, Maosheng Zhong, ZhiXiang Wang, Gen Liu, GanLin Liu, YingJian Xiao
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引用次数: 0
Context-Encoded Code Change Representation for Automated Commit Message Generation 用于自动提交消息生成的上下文编码的代码更改表示
4区 计算机科学 Q3 Computer Science Pub Date : 2023-09-16 DOI: 10.1142/s0218194023500493
Thanh Trong Vu, Thanh-Dat Do, Hieu Dinh Vo
Changes in source code are an inevitable part of software development. They are the results of indispensable activities such as fixing bugs or improving functionality. Descriptions for code changes (commit messages) help people better understand the changes. However, due to the lack of motivation and time pressure, writing high-quality commit messages remains reluctantly considered. Several methods have been proposed with the aim of automated commit message generation. However, the existing methods are still limited because they only utilize either the changed codes or the changed codes combined with their surrounding statements. This paper proposes a method to represent code changes by combining the changed codes and the unchanged codes which have program dependence on the changed codes. Specifically, we first create program dependence graphs (PDGs) of source code before and after the change. After that, slices related to the changed code from these PDGs are extracted. These slices are then merged to represent the change. This method overcomes the limitations of current representations while improving the performance of 5/6 of state-of-the-art commit message generation methods by up to 15% in METEOR, 14% in ROUGE-L, and 10% in BLEU-4.
更改源代码是软件开发中不可避免的一部分。它们是修复错误或改进功能等不可缺少的活动的结果。代码变更的描述(提交消息)帮助人们更好地理解变更。然而,由于缺乏动力和时间压力,编写高质量的提交消息仍然是不情愿的。为了实现自动提交消息的生成,已经提出了几种方法。然而,现有的方法仍然是有限的,因为它们要么只利用改变后的代码,要么只利用改变后的代码与它们周围的语句结合使用。本文提出了一种将变更码与对变更码有程序依赖的未变更码结合起来表示代码变更的方法。具体来说,我们首先在更改前后创建源代码的程序依赖关系图(PDGs)。之后,从这些pdg中提取与更改后的代码相关的片段。然后合并这些片以表示更改。该方法克服了当前表示的局限性,同时将5/6的最先进的提交消息生成方法的性能在METEOR中提高15%,在ROUGE-L中提高14%,在BLEU-4中提高10%。
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引用次数: 0
DTester: Diversity-driven Test Case Generation for Web Applications DTester:为Web应用程序生成多样性驱动的测试用例
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2023-09-05 DOI: 10.1142/s0218194023500559
Shumei Wu, Zexing Chang, Zhanwen Zhang, Zheng Li, Y. Liu
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引用次数: 0
ICG: A Machine Learning Benchmark Dataset and Baselines for Inline Code Comments Generation Task ICG:用于内联代码注释生成任务的机器学习基准数据集和基线
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2023-09-05 DOI: 10.1142/s0218194023500547
Xiaowei Zhang, Lin Chen, Weiqin Zou, Yulu Cao, Hao Ren, Zhi Wang, Yanhui Li, Yuming Zou
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引用次数: 0
Unifying Behavioral and Feature Modeling for Testing of Software Product Lines 统一软件产品线测试的行为和特征建模
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2023-08-25 DOI: 10.1142/s021819402350050x
F. Belli, Tugkan Tuglular, Ekincan Ufuktepe
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引用次数: 0
NNTBFV: Simplifying and Verifying Neural Networks using Testing-Based Formal Verification NNTBFV:使用基于测试的形式化验证简化和验证神经网络
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2023-08-25 DOI: 10.1142/s0218194023500523
Haiying Liu, Shaoying Liu, Guangquan Xu, A. Liu, Dingbang Fang
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引用次数: 0
Formalization and Verification of Enhanced Group Communication CoAP 增强型群组通信CoAP的形式化与验证
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2023-08-25 DOI: 10.1142/s0218194023500535
Sin-Hao Chen, Ran Li, Huibiao Zhu
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引用次数: 0
A Business-Oriented Methodology to Evaluate the Security of Software Architecture Quantitatively 一种面向业务的软件架构安全性定量评估方法
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2023-08-25 DOI: 10.1142/s0218194023500511
Hao Chen, Shengyang Zhou, Chen Chen, Zheng Dai, Bixin Li
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引用次数: 0
Enhancing Multi-Behavior Recommendations through Capturing Dynamic Preferences 通过捕获动态偏好来增强多行为推荐
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2023-08-17 DOI: 10.1142/s0218194023410012
Cairong Yan, Xiaopeng Guan, Haixia Han, Zhaohui Zhang, Yanting Zhang
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引用次数: 0
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International Journal of Software Engineering and Knowledge Engineering
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