DLBT:Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code

Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax or programming language technologies.However,writing Pseudo-code to each code instruction is laborious.Recently,neural machine translation is used to generate textual descriptions for the source code.In this paper,a novel deep learning-based transformer(DLBT)model is proposed for automatic Pseudo-code generation from the source code.The proposed model uses deep learning which is based on Neural Machine Translation(NMT)to work as a language translator.The DLBT is based on the transformer which is an encoder-decoder structure.There are three major components:tokenizer and embeddings,transformer,and post-processing.Each code line is tokenized to dense vector.Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network(RNN).At the post-processing step,the generated Pseudo-code is optimized.The proposed model is assessed using a real Python dataset,which contains more than 18,800 lines of a source code written in Python.The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network(RNN).The proposed DLBT records 47.32,68.49 accuracy and BLEU performance measures,respectively.

Achieving strength-ductility synergy in novel paramagnetic Fe-based medium-entropy alloys through deep cryogenic deformation

Cryogenic pre-deformation treatment has been widely used to effectively improve the comprehensive mechanical properties of steels and novel metals.However,the dislocation evolution and phase transformation induced by different degrees of deep cryogenic deformation are not yet fully elucidated.In this study,the effects of multiple cryogenic pre-treatments on the mechanical properties and deformation mechanisms of a paramagnetic Fe_(63.3)Mn_(14-)Si_(9.1)Cr_(9.8)C_(3.8)medium-entropy alloy(MEA)were investigated,leading to the discovery of a pretreated MEA that exhibits exceptional mechanical properties,including a fracture strength of 3.0 GPa,plastic strain of 26.1%and work-hardening index of 0.57.In addition,X-ray diffraction(XRD)and transmission electron microscopy(TEM)analyses revealed that multiple cryogenic pre-deformation treatments significantly increased the dislocation density of the MEA(from 9×10^(15)to 4×10^(16)m^(-2)after three pretreatments),along with a transition in the dislocation type from predominantly edge dislocations to mixed dislocations(including screw-and edge-type dislocations).Notably,this pretreated MEA retained its paramagnetic properties(μ_(r)<1.0200)even after fracture.Thermodynamic calculations showed that cryogenic pretreatment can significantly reduce the stacking fault energy of the MEA by a factor of approximately four(i.e.,from 9.7 to2.6 m J·m^(-2)),thereby activating the synergistic effects of transformation-induced plasticity,twinning-induced plasticity and dislocation strengthening mechanisms.These synergistic effects lead to simultaneous strength and ductility enhancement of the MEA.