연구 분야: Verification
학회: 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA)
The quality of automotive electronic software is critical to vehicle performance and safety, and it is of great significance to study relevant effective code analysis methods and testing tools to ensure software quality. A multi-static analyzer-fusion code analysis technique is proposed in this paper to detect problems such as defects and vulnerabilities in code, and generate readable reports to help developers identify and solve such challenges. In order to verify the effectiveness of this method, this article developed a code check based automotive electronic software testing tool for analyzing potential quality issues, tested multiple actual automotive electronic software projects, and compared it with four mainstream code testing tools. Results indicate that the self-developed tool can identify 90% of potential problems, reduce testing time by an average of 20%, and have high consistency with manual review results.
| 발행 연도 | 2023년 |
|---|---|
| 인용수 | 1 |
| 출판 국가 | China |
| 사이트 | IEEE |
| 좋아요 수 | 0 |