  Code reviews are one of the effective methods to estimate defectiveness in
source code. However, the existing methods are dependent on experts or
inefficient. In this paper, we improve the performance (in terms of speed and
memory usage) of our existing code review assisting tool--CRUSO. The central
idea of the approach is to estimate the defectiveness for an input source code
by using the defectiveness score of similar code fragments present in various
StackOverflow (SO) posts.
  The significant contributions of our paper are i) SOpostsDB: a dataset
containing the PVA vectors and the SO posts information, ii) CRUSO-P: a code
review assisting system based on PVA models trained on \emph{SOpostsDB}. For a
given input source code, CRUSO-P labels it as {Likely to be defective, Unlikely
to be defective, Unpredictable}. To develop CRUSO-P, we processed >3 million SO
posts and 188200+ GitHub source files. CRUSO-P is designed to work with source
code written in the popular programming languages {C, C#, Java, JavaScript, and
Python}.
  CRUSO-P outperforms CRUSO with an improvement of 97.82% in response time and
a storage reduction of 99.15%. CRUSO-P achieves the highest mean accuracy score
of 99.6% when tested with the C programming language, thus achieving an
improvement of 5.6% over the existing method.
