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 learning based approaches 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. We believe our observations in this paper could offer implications on the automated detection and repair of SStuBs.