@inproceedings{4cb98dd90f084a1e9eebe33f7ec54e53,
title = "A Software Defect Location Method based on Static Analysis Results",
abstract = "Code-graph based software defect prediction methods have become a research focus in SDP field. Among them, Code Property Graph is used as a form of data representation for code defects due to its ability to characterize the structural features and dependencies of defect codes. However, since the coarse granularity of Code Property Graph, redundant information which is not related to defects often attached to the characterization of software defects. Thus, it is a problem to be solved in how to locate software defects at a finer granularity in Code Property Graph. Static analysis is a technique for identifying software defects using set defect rules, and there are many proven static analysis tools in the industry. In this paper, we propose a method for locating specific types of defects in the Code Property Graph based on the result of static analysis tool. Experiments show that the location method based on static analysis results can effectively predict the location of specific defect types in real software program.",
keywords = "Code Property Graph, Defect Location, Software defect prediction, Static analysis",
author = "Haoxiang Shi and Wu Liu and Jingyu Liu and Jun Ai and Chunhui Yang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 9th International Conference on Dependable Systems and Their Applications, DSA 2022 ; Conference date: 04-08-2022 Through 05-08-2022",
year = "2022",
doi = "10.1109/DSA56465.2022.00124",
language = "English",
series = "Proceedings - 2022 9th International Conference on Dependable Systems and Their Applications, DSA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "876--886",
booktitle = "Proceedings - 2022 9th International Conference on Dependable Systems and Their Applications, DSA 2022",
address = "United States",
}