Design of an efficient Transformer-XL model for enhanced pseudo code to Python code conversion

Abstract

Thelandscape  of  programming  has  long  been  challenged  by  the  task  of transforming   pseudo   code   into   executable   Python   code,   a   process traditionally marred by its labor-intensive nature and the necessity for a deep understanding  of  both  logical  frameworks  and  programming  languages. Existing  methodologies often  grapple  with limitations  in handling  variable-length   sequences   and   maintaining   context   over   extended   textual   data. Addressing  these  challenges,  this  study  introduces  an  innovative  approach utilizing  the   Transformer-XL  model,   a   significant   advancement   in   the domain of deep learning. The Transformer-XL architecture, an evolution of the  standard  Transformer,  adeptly  processes  variable-length  sequences  and captures    extensive    contextual    dependencies,    thereby    surpassing    its predecessors  in   handling   natural   language  processing   (NLP)  and   code synthesis  tasks.  The  proposed  model  employs  a  comprehensive  process involving   data   preprocessing,   model   input   encoding,   a   self-attention mechanism,  contextual  encoding,  language  modeling,  and  a  meticulous decoding  process,  followed  by  post-processing.  The  implications  of  this work are far-reaching, offering a substantial leap in the automation of code conversion. As the field of NLP and deep learning continues to evolve, the Transformer-XL  based  model  is  poised  to  become  an  indispensable  tool in the  realm  of  programming,  setting  a  new  benchmark  for  automated  code synthesis.