Nowadays, application developers use software bug detection techniques widely. One of the most popular bug detection techniques is static analysis which is a pattern-based method. In such techniques patterns are manually created by experts. Despite creating a huge amount of patterns for various bugs, there are still many bugs that pass through all the available filters. In this paper, a new approach is presented for automatic bug detection in JavaScript codes. We map the buggy and bug-free codes to graphs. Then a deep learning model which accepts graphs is trained to classify codes to buggy and bug-free. The evaluation results show that the proposed method covers a wider range of bugs while outperforms previous methods.
Yousofvand, L., Soleimani, S., & esfandyari, S. (2024). Bug detection Using model transformations and deep learning. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, (), -. doi: 10.22034/tjee.2024.57018.4645
MLA
Leila Yousofvand; Seyfollah Soleimani; sajad esfandyari. "Bug detection Using model transformations and deep learning". TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, , , 2024, -. doi: 10.22034/tjee.2024.57018.4645
HARVARD
Yousofvand, L., Soleimani, S., esfandyari, S. (2024). 'Bug detection Using model transformations and deep learning', TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, (), pp. -. doi: 10.22034/tjee.2024.57018.4645
VANCOUVER
Yousofvand, L., Soleimani, S., esfandyari, S. Bug detection Using model transformations and deep learning. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 2024; (): -. doi: 10.22034/tjee.2024.57018.4645
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