Preprint Open Access

On the Effectiveness of Deep Vulnerability Detectors to Simple Stupid Bug Detection

Hua, Jiayi; Wang, Haoyu

Recent studies have shown the promising direction of deep learning based bug detection, which relieves human experts from the tedious and subjective task of manually summarizing features. Simple one-statement bugs (i.e., SStuBs), which occur relatively often in Java projects, cannot be well spotted by existing static analysis tools. In this paper, we make effort to empirically analyze whether deep learning based techniques could be used to detecting SStuBs. We have re-implemented two state-of-the-art techniques in approximately 3,000 lines of code and adopted them to detecting Java SStuBs. Experiments on large-scale datasets suggest that although deep vulnerability detectors can achieve much better results than existing static analyzers, the SStuBs cannot be well flagged when comparing with traditional complex vulnerabilities. We further look in detail on the per bug category basis, observing that deep learning based methods perform better when detecting some specific types of bugs (e.g., ``Same Function Change Caller''), which have strong data flow and control flow semantic. Our observations could offer implications on the automated detection and repair of SStuBs.

Files (269.4 kB)
Name Size
On the Effectiveness of Deep Vulnerability Detectors to Simple Stupid Bug Detection.pdf
md5:3b0cb95c2942c74c3d6045f9b7cb528d
269.4 kB Download
329
251
views
downloads
All versions This version
Views 329329
Downloads 251251
Data volume 67.6 MB67.6 MB
Unique views 309309
Unique downloads 231231

Share

Cite as