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A Multi-Feature Fusion-Based Automatic Detection Method for High-Severity Defects

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成果类型:
期刊论文
作者:
Liu, Jie;Liang, Cangming;Feng, Jintao;Xiao, Anhong;Zeng, Hui;...
通讯作者:
Liu, J
作者机构:
[Liu, Jie; Liang, Cangming; Liu, J; Yu, Tonglan; Wu, Qujin] Univ South China, Dept Comp Sci, Hengyang 421001, Peoples R China.
[Xiao, Anhong; Zeng, Hui; Feng, Jintao] Nucl Power Inst China, Chengdu 610213, Peoples R China.
通讯机构:
[Liu, J ] U
Univ South China, Dept Comp Sci, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
high-severity defect;contextual features;machine learning;multi-feature fusion
期刊:
Electronics
ISSN:
2079-9292
年:
2023
卷:
12
期:
14
页码:
3075
基金类别:
National Natural Science Foundation of China#&#&#62003157 Research Foundation of Education Bureau of Hunan Province#&#&#22C0223#&#&#21B0434
机构署名:
本校为第一且通讯机构
院系归属:
计算机科学与技术学院
摘要:
It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label metho...

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