With the immense progress in Machine Learning in the past decades, General Machine Learning(GLM) models have increased to perform many different tasks such, grammar correction , classification of items or explain pieces of code. One of these new fields that GLM models can achieve is the Automated Program Repair(APR) helping developers by generating patches to automatically repair software bugs. Typically these APR techniques are based on a pre-trained neural network mode, trained by pairs of codes with errors and their correction. These models take as input a buggy code and return a regenerated code with the defects repaired. Nowadays these are one of the most effective techniques for automatic correction, but these DeepLearning-based APR approaches still present many errors on a large portion of the codes given to the models. Alternatively Code language models (CLMs), has become a good solution when APR problems are involved, this CLMs differ from the DeepLearing(DL) ones because CLMS are not trained from a set of buggy codes and correct ones, these models are trained from a huge source of unlabeled codes for general code language modeling tasks. The aim of this research is to know the capabilities and the shortcomings of Code Language Models in the automatic program repair field and see how developers can take advantage of these new techniques and use them in order to improve their programming skills, saving time and money
