Abstract
Data detailed on machine translation have been presented. According to news originating from Zhengzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Automatic program repair (APR) is crucial to improve software quality. Recently, neural machine translation (NMT) based modeling for bug fixes has demonstrated great potential.” The news reporters obtained a quote from the research from Henan University of Technology: “However, these approaches still have two major challenges. One is that their search space is limited due to the out-ofvocabulary (OOV) problem. The other is that the NMT-based APR models tend to ignore past translation information, which often leads to over-translation and under-translation. To address the above challenges, we propose MNRepair, a new NMT-based APR approach that combines multiple mechanisms to fix bugs in source code. Specifically, we devise an encoder-decoder NMT framework with the attention mechanism. Our framework combines the copy mechanism to overcome the OOV problem that occurs with source code.”
NETL
NSTL