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There so many applications available on the internet (Especially in Google play store), which many are using for all types of different purposes. Many users gives their reviews about the applications they used like their experience, the issues they face, the updates which will be helpful for them, etc. All of these reviews should be analysed so that the developer can improve the applications by adding required updates needed by the users, and fixing issues faced by the users. For this purpose we need to analyse the reviews in an efficient manner which will save more time and improve their applications. In this project we are doing classification of bugs in reviews with the help of encoder based BERT(Bidirectional Encoder Representations from Transformers) model,which have been very popular in natural language processing, has been widely used now a days. This will help us to classify the reviews in a manner that we can identify positive and negative reviews which will help the developers to find the bugs. The reports that are under negative and neutral reviews are considered for bugs. It is found that BERT model is much efficient in text classification so it will be very well suitable for review based classification. There four different phases in this project first one is the data exploration, then we pre-process the data, and then do feature selection, finally we classify the reviews using BERT model.