Title: Automatic detection of contextual defects based on machine learning

Authors: Cangming Liang; Jie Liu; Jintao Feng; Anhong Xiao; Hui Zeng; Qujin Wu; Tonglan Yu

Addresses: University of South China, Hunan, China ' University of South China, Hunan, China ' Nuclear Power Institute of China, Sichuan, China ' Nuclear Power Institute of China, Sichuan, China ' Nuclear Power Institute of China, Sichuan, China ' University of South China, Hunan, China ' University of South China, Hunan, China

Abstract: In recent years, automatic detection technology has been widely used in software defect detection, which significantly reduces the cost of manual inspection. Machine learning is one of the common automatic detection technologies. By extracting defect features and using supervised classification algorithms to automatically identify possible software doubtful defects, the key is to design an automatic verification model with high accuracy. This paper focuses on the contextual defects related to control flow, and uses the dynamic checking method in the static testing process. By using control flow to represent the context information of code, the node features and basic path features in control flow graph are proposed, and a new defect automatic verification model based on SVM is designed. The experimental results show that the proposed model has higher recall and F1 than the existing methods, and significantly improves the efficiency of automatic identification of suspected defects.

Keywords: automatic defect detection; machine learning; support vector machine; SVM; contextual defect.

DOI: 10.1504/IJES.2023.134124

International Journal of Embedded Systems, 2023 Vol.16 No.1, pp.75 - 82

Received: 09 Jan 2023
Accepted: 30 Mar 2023
Published online: 11 Oct 2023 *

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