  Deep learning had been used in program analysis for the prediction of hidden
software defects using software defect datasets, security vulnerabilities using
generative adversarial networks as well as identifying syntax errors by
learning a trained neural machine translation on program codes. However, all
these approaches either require defect datasets or bug-free source codes that
are executable for training the deep learning model. Our neural network model
is neither trained with any defect datasets nor bug-free programming source
codes, instead it is trained using structural semantic details of Abstract
Syntax Tree (AST) where each node represents a construct appearing in the
source code. This model is implemented to fix one of the most common semantic
errors, such as undeclared variable errors as well as infer their type
information before program compilation. By this approach, the model has
achieved in correctly locating and identifying 81% of the programs on prutor
dataset of 1059 programs with only undeclared variable errors and also
inferring their types correctly in 80% of the programs.
